Browsing by Author "Neeraj Sharma"

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A comparison between revised Manchester Point A and ICRU-89–recommended Point A definition absorbed-dose reporting using CT images in intracavitary brachytherapy for patients with cervical carcinoma
(Elsevier Inc., 2021) Ankur Mourya; Sunil Choudhary; Uday Pratap Shahi; Neeraj Sharma; Himani Gautam; Ganeshkumar Patel; Satyajit Pradhan; Lalit Mohan Aggarwal
Purpose: This study is a comparison between revised Manchester Point A and International Commission on Radiation Units and measurements (ICRU) 89 report–recommended Point A absorbed-dose reporting in intracavitary brachytherapy for patients with cervical carcinoma. Methods and Materials: The retrospective dosimetric study is based on the data of 32 patients with cervical carcinoma treated with high-dose-rate brachytherapy. Patients received 21 Gy in three fractions (7.0 Gy X three fractions) to Point A (Aflange, revised Manchester definition). All the patients were replanned with a new Point A (Aicru89) defined on CT images as per the American Brachytherapy Society/ICRU-89. The data collected were compared with the data obtained from Point A (Aflange). Results: When using the Aflange plan normalization method, the mean dose of 0.1 cc, 1 cc, and 2 cc bladder volumes was 820.79 ± 207.47 cGy, 654.66 ± 152.69 cGy, and 588.91 ± 136.35 cGy, respectively. Likewise, when using the ICRU-89 Point Aicru89 normalization method, the mean dose of 0.1 cc, 1 cc, and 2 cc bladder volumes was 869.30 ± 224.67 cGy, 693.24 ± 166.20 cGy, and 616.61 ± 150.32 cGy, respectively. For the rectum, Point Aflange normalization plans, the mean dose of 0.1 cc, 1 cc, and 2 cc volumes was 589.37 ± 163.26 cGy, 487.51 ± 126.03 cGy, and 442.70 ± 111.43 cGy, respectively. Likewise, using the Aicru89 plan, the mean 0.1 cc, 1 cc, and 2 cc rectum volume was 625.07 ± 171.31 cGy, 517.50 ± 131.05 cGy, 464.94 ± 121.81 cGy, respectively. The statistical mean difference of Total Reference Air Kerma rate, V100 (cc), bladder, rectum and sigmoid, was found significant. Conclusions: It has been found that the position of revised Manchester (Aflange) and ICRU-89 Point A does not match on CT images/radiograph, which resulted in variation in doses to the tumor, V100 (cc), organ at risk, and Total Reference Air Kerma. © 2021 American Brachytherapy Society
A mathematical model to predict the different isodose volumes using TRAK value in HDR intracavitary brachytherapy for revised Manchester and ICRU-89 based Point A plans using computer tomography images
(Wolters Kluwer Medknow Publications, 2022) Ankur Mourya; Sunil Choudhary; Neeraj Sharma; Uday Pratap Shahi; Gaganpreet Singh; Satyajit Pradhan; Lalit Mohan Aggarwal
Purpose: To find out the simple relationship between Total Reference Air Kerma (TRAK) and various isodose volumes. Calculated isodose volumes were compared with experimental data for revised Manchester and International Commission on Radiation Units and measurements (ICRU)-89 Point A-based treatment plans. The accuracy of the formula was compared with the results of other relationships available in the literature. Materials and Methods: Dosimetric data from 62 intracavitary brachytherapy (ICBT) treatment plans of 31 patients with cervical cancer were studied. Each patient had treatment plans normalized to revised Manchester and ICRU-89 Points A (A flange and A icru89). For each treatment plan, TRAK values, V 350, V 700, V 1050, and V 1400 were obtained. The modeling curve was plotted between Isodose volume (V d) and the ratio of d/TRAK obtained from A flange plans to get a mathematical relation. The results of this formula were compared with the experimental data and outcomes of other formulas available in the literature. A paired-sample t-Test was performed to assess the statistical significance. Results: In the case of revised Manchester-based A flange normalization plans, the mean isodose volume of V 350, V 700, V 1050, and V 1400 were 285.98 ± 32.3 cm 3, 101.96 ± 10.63 cm 3, 52.71 ± 4.72 cm 3, and 31.44 ± 2.33 cm 3 respectively. Likewise, for ICRU-89 based A icru89 normalization plans, the mean isodose volumes of V 350, V 700, V 1050, and V 1400 were 304.11 ± 26.17 cm 3, 108.88 ± 8.29 cm 3, 56.62 ± 3.69 cm 3 and 34 ± 2.23 cm 3 respectively. The mean difference was significant. The Mathematical relationship developed was [INLINE:1]. No correlation was found between TRAK and D 0.1cm 3,D 2cm 3 for organs at risk. Conclusions: The developed formula calculated isodose volumes within the accuracy of ± 3% in ICBT plans. © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
A Monte Carlo simulation-based decision support system for radiation oncologists in the treatment of glioblastoma multiforme
(Springer Science and Business Media Deutschland GmbH, 2024) C. Praveen Kumar; Lalit M. Aggarwal; Saju Bhasi; Neeraj Sharma
In the present research, we have developed a model-based crisp logic function statistical classifier decision support system supplemented with treatment planning systems for radiation oncologists in the treatment of glioblastoma multiforme (GBM). This system is based on Monte Carlo radiation transport simulation and it recreates visualization of treatment environments on mathematical anthropomorphic brain (MAB) phantoms. Energy deposition within tumour tissue and normal tissues are graded by quality audit factors which ensure planned dose delivery to tumour site thereby minimising damages to healthy tissues. The proposed novel methodology predicts tumour growth response to radiation therapy from a patient-specific medicine quality audit perspective. Validation of the study was achieved by recreating thirty-eight patient-specific mathematical anthropomorphic brain phantoms of treatment environments by taking into consideration density variation and composition of brain tissues. Dose computations accomplished through water phantom, tissue-equivalent head phantoms are neither cost-effective, nor patient-specific customized and is often less accurate. The above-highlighted drawbacks can be overcome by using open-source Electron Gamma Shower (EGSnrc) software and clinical case reports for MAB phantom synthesis which would result in accurate dosimetry with due consideration to the time factors. Considerable dose deviations occur at the tumour site for environments with intraventricular glioblastoma, haematoma, abscess, trapped air and cranial flaps leading to quality factors with a lower logic value of 0. Logic value of 1 depicts higher dose deposition within healthy tissues and also leptomeninges for majority of the environments which results in radiation-induced laceration. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Adolescent transport and unintentional injuries: a systematic analysis using the Global Burden of Disease Study 2019
(Elsevier Ltd, 2022) Amy E. Peden; Patricia Cullen; Kate Louise Francis; Holger Moeller; Margaret M. Peden; Pengpeng Ye; Maoyi Tian; Zhiyong Zou; Susan M. Sawyer; Amirali Aali; Zeinab Abbasi-Kangevari; Mohsen Abbasi-Kangevari; Michael Abdelmasseh; Meriem Abdoun; Rami Abd-Rabu; Deldar Morad Abdulah; Ame Mehadi Abdurehman; Getachew Abebe; Ayele Mamo Abebe; Aidin Abedi; Hassan Abidi; Richard Gyan Aboagye; Hiwa Abubaker Ali; Eman Abu Gharbieh; Denberu Eshetie Adane; Tigist Demssew Adane; Isaac Yeboah Addo; Ololade Grace Adewole; Sangeet Adhikari; Mohammad Adnan; Qorinah Estiningtyas Sakilah Adnani; Aanuoluwapo Adeyimika Bachelor Afolabi; Saira Afzal; Muhammad Sohail Afzal; Zahra Babaei Aghdam; Bright Opoku Ahinkorah; Araz Ramazan Ahmad; Tauseef Ahmad; Sajjad Ahmad; Ali Ahmadi; Haroon Ahmed; Muktar Beshir Ahmed; Ali Ahmed; Ayman Ahmed; Jivan Qasim Ahmed; Tarik Ahmed Rashid; Janardhana P. Aithala; Budi Aji; Meisam Akhlaghdoust; Fares Alahdab; Fahad Mashhour Alanezi; Astawus Alemayehu; Hanadi Al Hamad; Syed Shujait Ali; Liaqat Ali; Yousef Alimohamadi; Vahid Alipour; Syed Mohamed Aljunid; Louay Almidani; Sami Almustanyir; Khalid A. Altirkawi; Nelson J. Alvis-Zakzuk; Edward Kwabena Ameyaw; Tarek Tawfik Amin; Mehrdad Amir-Behghadami; Sohrab Amiri; Hoda Amiri; Tadele Fentabil Anagaw; Tudorel Andrei; Catalina Liliana Andrei; Davood Anvari; Sumadi Lukman Anwar; Anayochukwu Edward Anyasodor; Jalal Arabloo; Morteza Arab-Zozani; Asrat Arja; Judie Arulappan; Ashokan Arumugam; Armin Aryannejad; Saeed Asgary; Tahira Ashraf; Seyyed Shamsadin Athari; Alok Atreya; Sameh Attia; Avinash Aujayeb; Atalel Fentahun Awedew; Sina Azadnajafabad; Mohammadreza Azangou-Khyavy; Samad Azari; Amirhossein Azari Jafari; Hosein Azizi; Ahmed Y. Azzam; Ashish D. Badiye; Nayereh Baghcheghi; Sara Bagherieh; Atif Amin Baig; Shankar M. Bakkannavar; Asaminew Birhanu Balta; MacIej Banach; Palash Chandra Banik; Hansi Bansal; Mainak Bardhan; Francesco BaroneAdesi; Amadou Barrow; Azadeh Bashiri; Pritish Baskaran; Saurav Basu; Nebiyou Simegnew Bayileyegn; Abebe Ayalew Bekel; Alehegn Bekele Bekele; Salaheddine Bendak; Isabela M. Bensenor; Alemshet Yirga Berhie; Devidas S. Bhagat; Akshaya Srikanth Bhagavathula; Pankaj Bhardwaj; Nikha Bhardwaj; Sonu Bhaskar; Ajay Nagesh Bhat; Krittika Bhattacharyya; Zulfiqar A. Bhutta; Sadia Bibi; Bagas Suryo Bintoro; Saeid Bitaraf; Belay Boda Abule Bodicha; Archith Boloor; Souad Bouaoud; Julie Brown; Katrin Burkart; Nadeem Shafique Butt; Muhammad Hammad Butt; Luis Alberto CÃ¡mera; Julio Cesar Campuzano Rincon; Chao Cao; Andre F. Carvalho; MÃ¡rcia Carvalho; Promit Ananyo Chakraborty; Eeshwar K. Chandrasekar; Jung-Chen Chang; Periklis Charalampous; Jaykaran Charan; Vijay Kumar Chattu; Bitew Mekonnen Chekole; Abdulaal Chitheer; Daniel Youngwhan Cho; Hitesh Chopra; Devasahayam J. Christopher; Isaac Sunday Chukwu; NatÃ¡lia Cruz-Martins; Omid Dadras; Saad M.A. Dahlawi; Xiaochen Dai; Giovanni Damiani; Gary L. Darmstadt; Reza Darvishi Cheshmeh Soltani; Aso Mohammad Darwesh; Saswati Das; Anna Dastiridou; Sisay Abebe Debela; Amin Dehghan; Getnet Makasha Demeke; Andreas K. Demetriades; Solomon Demissie; Fikadu Nugusu Dessalegn; Abebaw Alemayehu Desta; Mostafa Dianatinasab; Nancy Diao; Diana Dias Da Silva; Daniel Diaz; Lankamo Ena Digesa; Mengistie Diress; Shirin Djalalinia; Linh Phuong Doan; Milad Dodangeh; Paul Narh Doku; Deepa Dongarwar; Haneil Larson Dsouza; Ebrahim Eini; Michael Ekholuenetale; Temitope Cyrus Ekundayo; Ahmed Elabbas Mustafa Elagali; Mostafa Ahmed Elbahnasawy; Hala Rashad Elhabashy; Muhammed Elhadi; Maysaa El Sayed Zaki; Daniel Berhanie Enyew; Ryenchindorj Erkhembayar; Sharareh Eskandarieh; Farshid Etaee; Adeniyi Francis Fagbamigbe; Pawan Sirwan Faris; Abbas Farmany; Andre Faro; Farshad Farzadfar; Ali Fatehizadeh; Seyed Mohammad Fereshtehnejad; Abdullah Hamid Feroze; Getahun Fetensa; Bikila Regassa Feyisa; Irina Filip; Florian Fischer; Behzad Foroutan; Masoud Foroutan; Kayode Raphael Fowobaje; Richard Charles Franklin; Takeshi Fukumoto; Peter Andras Gaal; Muktar A. Gadanya; Yaseen Galali; Nasrin Galehdar; Balasankar Ganesan; Tushar Garg; Mesfin Gebrehiwot Damtew Gebrehiwot; Yosef Haile Gebremariam; Yibeltal Yismaw Gela; Urge Gerema; Mansour Ghafourifard; Seyyed-Hadi Ghamari; Reza Ghanbari; Mohammad Ghasemi Nour; Maryam Gholamalizadeh; Ali Gholami; Ali Gholamrezanezhad; Sherief Ghozy; Syed Amir Gilani; Tiffany K. Gill; Iago GinÃ©-VÃ¡zquez; Zeleke Abate Girma; James C. Glasbey; Franklin N. Glozah; Mahaveer Golechha; Pouya Goleij; Michal Grivna; Habtamu Alganeh Guadie; Damitha Asanga Gunawardane; Yuming Guo; Veer Bala Gupta; Sapna Gupta; Bhawna Gupta; Vivek Kumar Gupta; Arvin Haj-Mirzaian; Rabih Halwani; Randah R. Hamadeh; Sajid Hameed; Lolemo Kelbiso Hanfore; Asif Hanif; Arief Hargono; Netanja I. Harlianto; Mehdi Harorani; Ahmed I. Hasaballah; S.M. Mahmudul Hasan; Amr Hassan; Soheil Hassanipour; Hadi Hassankhani; Rasmus J. Havmoeller; Simon I. Hay; Mohammad Heidari; Delia Hendrie; Demisu Zenbaba Heyi; Yuta Hiraike; Ramesh Holla; Nobuyuki Horita; Sheikh Jamal Hossain; Mohammad Bellal Hossain Hossain; Sedighe Hosseini Shabanan; Mehdi Hosseinzadeh; Sorin Hostiuc; Amir Human Hoveidaei; Alexander Kevin Hsiao; Salman Hussain; Amal Hussein; Segun Emmanuel Ibitoye; Olayinka Stephen Ilesanmi; Irena M. Ilic; Milena D. Ilic; Behzad Imani; Mustapha Immurana; Leeberk Raja Inbaraj; Sheikh Mohammed Shariful Islam; Rakibul M. Islam; Mohammad Mainul Islam; Nahlah Elkudssiah Ismail; J. Linda Merin; Haitham Jahrami; Mihajlo Jakovljevic; Manthan Dilipkumar Janodia; Tahereh Javaheri; Sathish Kumar Jayapal; Umesh Umesh Jayarajah; Sudha Jayaraman; Jayakumar Jeganathan; Bedru Jemal; Ravi Prakash Jha; Jost B. Jonas; Tamas Joo; Nitin Joseph; Jacek Jerzy Jozwiak; Mikk JÃ¼risson; Ali Kabir; Vidya Kadashetti; Dler Hussein Kadir; Laleh R. Kalankesh; Leila R. Kalankesh; Rohollah Kalhor; Vineet Kumar Kamal; Rajesh Kamath; Himal Kandel; Rami S. Kantar; Neeti Kapoor; Hassan Karami; Ibraheem M. Karaye; Samad Karkhah; Patrick D.M.C. Katoto; Joonas H. Kauppila; Gbenga A. Kayode; Leila Keikavoosi-Arani; Cumali Keskin; Yousef Saleh Khader; Himanshu Khajuria; Mohammad Khammarnia; Ejaz Ahmad Khan; Md Nuruzzaman Khan; Maseer Khan; Yusra H. Khan; Imteyaz A. Khan; Abbas Khan; Moien A.B. Khan; Javad Khanali; Moawiah Mohammad Khatatbeh; Hamid Reza Khayat Kashani; Habibolah Khazaie; Jagdish Khubchandani; Zemene Demelash Kifle; Jihee Kim; Yun Jin Kim; Sezer Kisa; Adnan Kisa; Cameron J. Kneib; Farzad Kompani; Hamid Reza Koohestani; Parvaiz A. Koul; Sindhura Lakshmi Koulmane Laxminarayana; Ai Koyanagi; Kewal Krishan; Vijay Krishnamoorthy; Burcu Kucuk Bicer; Nithin Kumar; Naveen Kumar; Narinder Kumar; Manasi Kumar; Om P. Kurmi; Lucie Laflamme; Judit LÃ¡m; IvÃ¡n Landires; Bagher Larijani; Savita Lasrado; Paolo Lauriola; Carlo La Vecchia; Shaun Wen Huey Lee; Yo Han Lee; Sang-Woong Lee; Wei Chen Lee; Samson Mideksa Legesse; Shanshan Li; Stephen S. Lim; LÃ¡szlÃ³ Lorenzovici; Amana Ogeto Luke; Farzan Madadizadeh; Áurea M. Madureira-Carvalho; Muhammed Magdy Abd El Razek; Soleiman Mahjoub; Ata Mahmoodpoor; Razzagh Mahmoudi; Marzieh Mahmoudimanesh; Azeem Majeed; Alaa Makki; Elaheh Malakan Rad; Mohammad-Reza Malekpour; Ahmad Azam Malik; Tauqeer Hussain Mallhi; Deborah Carvalho Malta; Borhan Mansouri; Mohammad Ali Mansournia; Elezebeth Mathews; Sazan Qadir Maulud; Dennis Mazingi; Entezar Mehrabi Nasab; Oliver Mendoza-Cano; Ritesh G. Menezes; Dechasa Adare Mengistu; Alexios-Fotios A. Mentis; Atte Meretoja; Mohamed Kamal Mesregah; Tomislav Mestrovic; Ana Carolina Micheletti Gomide Nogueira de Sá; Ted R. Miller; Seyed Peyman Mirghaderi; Andreea Mirica; Seyyedmohammadsadeq Mirmoeeni; Erkin M. Mirrakhimov; Moonis Mirza; Sanjeev Misra; Prasanna Mithra; Chaitanya Mittal; Madeline E. Moberg; Mokhtar Mohammadi; Soheil Mohammadi; Esmaeil Mohammadi; Reza Mohammadpourhodki; Shafiu Mohammed; Teroj Abdulrahman Mohammed; Mohammad Mohseni; Ali H. Mokdad; Sara Momtazmanesh; Lorenzo Monasta; Mohammad Ali Moni; Rafael Silveira Moreira; Shane Douglas Morrison; Ebrahim Mostafavi; Haleh Mousavi Isfahani; Sumaira Mubarik; Lorenzo Muccioli; Soumyadeep Mukherjee; Francesk Mulita; Ghulam Mustafa; Ahamarshan Jayaraman Nagarajan; Mukhammad David Naimzada; Vinay Nangia; Hasan Nassereldine; Zuhair S. Natto; Biswa Prakash Nayak; Ionut Negoi; Seyed Aria Nejadghaderi; Samata Nepal; Sandhya Neupane Kandel; Nafise Noroozi; Virginia NuÃ±ez-Samudio; Ogochukwu Janet Nzoputam; Chimezie Igwegbe Nzoputam; Chimedsuren Ochir; Julius Nyerere Odhiambo; Oluwakemi Ololade Odukoya; Hassan Okati-Aliabad; Osaretin Christabel Okonji; Andrew T. Olagunju; Ahmed Omar Bali; Emad Omer; Adrian Otoiu; Stanislav S. Otstavnov; Nikita Otstavnov; Bilcha Oumer; Mayowa O. Owolabi; P.A. Mahesh; Alicia Padron-Monedero; Jagadish Rao Padubidri; Mohammad Taha Pahlevan Fallahy; Songhomitra Panda-Jonas; Seithikurippu R. Pandi-Perumal; Shahina Pardhan; Eun-Kee Park; Sangram Kishor Patel; Aslam Ramjan Pathan; Siddhartha Pati; Uttam Paudel; Shrikant Pawar; Paolo Pedersini; Mario F.P. Peres; Ionela-Roxana Petcu; Michael R. Phillips; Julian David Pillay; Zahra Zahid Piracha; Mohsen Poursadeqiyan; Naeimeh Pourtaheri; Ibrahim Qattea; Amir Radfar; Ata Rafiee; Pankaja Raghav Raghav; Fakher Rahim; Muhammad Aziz Rahman; Firman Suryadi Rahman; Mosiur Rahman; Amir Masoud Rahmani; Shayan Rahmani; Sheetal Raj Moolambally; Sheena Ramazanu; Kiana Ramezanzadeh; Juwel Rana; Saleem Muhammad Rana; Chythra R. Rao; Sowmya J. Rao; Vahid Rashedi; Mohammad Mahdi Rashidi; Prateek Rastogi; Azad Rasul; Salman Rawaf; David Laith Rawaf; Lal Rawal; Reza Rawassizadeh; Negar Rezaei; Nazila Rezaei; Mohsen Rezaeian; Aziz Rezapour; Abanoub Riad; Muhammad Riaz; Jennifer Rickard; Jefferson Antonio Buendia Rodriguez; Leonardo Roever; Luca Ronfani; Bedanta Roy; S. Manjula; Aly M.A. Saad; Siamak Sabour; Leila Sabzmakan; Basema Saddik; Malihe Sadeghi; Mohammad Reza Saeb; Umar Saeed; Sahar Saeedi Moghaddam; Sher Zaman Safi; Biniyam Sahiledengle; Harihar Sahoo; Mohammad Ali Sahraian; Morteza Saki; Payman Salamati; Sana Salehi; Marwa Rashad Salem; Abdallah M. Samy; Juan Sanabria; Milena M. Santric-Milicevic; Muhammad Arif Nadeem Saqib; Yaser Sarikhani; Arash Sarveazad; Brijesh Sathian; Maheswar Satpathy; Ganesh Kumar Saya; Ione Jayce Ceola Schneider; David C. Schwebel; Hamed Seddighi; Subramanian Senthilkumaran; Allen Seylani; Hosein Shabaninejad; Melika Shafeghat; Pritik A. Shah; Saeed Shahabi; Ataollah Shahbandi; Fariba Shahraki-Sanavi; Masood Ali Shaikh; Elaheh Shaker; Mehran Shams-Beyranvand; Mohd Shanawaz; Mohammed Shannawaz; Mequannent Melaku Sharew Sharew; Neeraj Sharma; Bereket Beyene Shashamo; Maryam Shayan; Rahim Ali Sheikhi; Jiabin Shen; B. Suresh Kumar Shetty; Pavanchand H. Shetty; Jae Il Shin; Nebiyu Aniley Shitaye; K.M. Shivakumar; Parnian Shobeiri; Seyed Afshin Shorofi; Sunil Shrestha; Soraya Siabani; Negussie Boti Sidemo; Wudneh Simegn; Ehsan Sinaei; Paramdeep Singh; Robert Sinto; Md Shahjahan Siraj; Valentin Yurievich Skryabin; Anna Aleksandrovna Skryabina; David A. Sleet; S.N. Chandan; Bogdan Socea; Marco Solmi; Yonatan Solomon; Yi Song; Raúl A.R.C. Sousa; Ireneous N. Soyiri; Mark A. Stokes; Muhammad Suleman; Rizwan Suliankatchi Abdulkader; Jing Sun; Rafael TabarÃ©s-Seisdedos; Seyyed Mohammad Tabatabaei; Mohammad Tabish; Ensiyeh Taheri; Moslem Taheri Soodejani; Mircea Tampa; KerKan Tan; Ingan Ukur Tarigan; Md Tariqujjaman; Nathan Y. Tat; Vivian Y. Tat; Arash Tavakoli; Belay Negash Tefera; Yibekal Manaye Tefera; Gebremaryam Temesgen; Mohamad-Hani Temsah; Pugazhenthan Thangaraju; Rekha Thapar; Nikhil Kenny Thomas; Jansje Henny Vera Ticoalu; Marius Belmondo Tincho; Amir Tiyuri; Munkhsaikhan Togtmol; Marcos Roberto Tovani-Palone; Mai Thi Ngoc Tran; Sana Ullah; Saif Ullah; Irfan Ullah; Srikanth Umakanthan; Bhaskaran Unnikrishnan; Era Upadhyay; Sahel Valadan Tahbaz; Pascual R. Valdez; Tommi Juhani Vasankari; Siavash Vaziri; Massimiliano Veroux; Dominique Vervoort; Francesco S. Violante; Vasily Vlassov; Linh Gia Vu; Yasir Waheed; Yanzhong Wang; Yuan-Pang Wang; Cong Wang; Taweewat Wiangkham; Nuwan Darshana Wickramasinghe; Abay Tadesse Woday; Ai-Min Wu; Gahin Abdulraheem Tayib Yahya; Seyed Hossein Yahyazadeh Jabbari; Lin Yang; Sanni Yaya; Arzu Yigit; Vahit Yigit; Eshetu Yisihak; Naohiro Yonemoto; Yuyi You; Mustafa Z. Younis; Chuanhua Yu; Ismaeel Yunusa; Hossein Yusefi; Mazyar Zahir; Sojib Bin Zaman; Iman Zare; Kourosh Zarea; Mikhail Sergeevich Zastrozhin; Zhi-Jiang Zhang; Yunquan Zhang; Arash Ziapour; Sanjay Zodpey; Mohammad Zoladl; George C. Patton; Rebecca Q. Ivers
Background: Globally, transport and unintentional injuries persist as leading preventable causes of mortality and morbidity for adolescents. We sought to report comprehensive trends in injury-related mortality and morbidity for adolescents aged 10–24 years during the past three decades. Methods: Using the Global Burden of Disease, Injuries, and Risk Factors 2019 Study, we analysed mortality and disability-adjusted life-years (DALYs) attributed to transport and unintentional injuries for adolescents in 204 countries. Burden is reported in absolute numbers and age-standardised rates per 100 000 population by sex, age group (10–14, 15–19, and 20–24 years), and sociodemographic index (SDI) with 95% uncertainty intervals (UIs). We report percentage changes in deaths and DALYs between 1990 and 2019. Findings: In 2019, 369 061 deaths (of which 214 337 [58%] were transport related) and 31·1 million DALYs (of which 16·2 million [52%] were transport related) among adolescents aged 10–24 years were caused by transport and unintentional injuries combined. If compared with other causes, transport and unintentional injuries combined accounted for 25% of deaths and 14% of DALYs in 2019, and showed little improvement from 1990 when such injuries accounted for 26% of adolescent deaths and 17% of adolescent DALYs. Throughout adolescence, transport and unintentional injury fatality rates increased by age group. The unintentional injury burden was higher among males than females for all injury types, except for injuries related to fire, heat, and hot substances, or to adverse effects of medical treatment. From 1990 to 2019, global mortality rates declined by 34·4% (from 17·5 to 11·5 per 100 000) for transport injuries, and by 47·7% (from 15·9 to 8·3 per 100 000) for unintentional injuries. However, in low-SDI nations the absolute number of deaths increased (by 80·5% to 42 774 for transport injuries and by 39·4% to 31 961 for unintentional injuries). In the high-SDI quintile in 2010–19, the rate per 100 000 of transport injury DALYs was reduced by 16·7%, from 838 in 2010 to 699 in 2019. This was a substantially slower pace of reduction compared with the 48·5% reduction between 1990 and 2010, from 1626 per 100 000 in 1990 to 838 per 100 000 in 2010. Between 2010 and 2019, the rate of unintentional injury DALYs per 100 000 also remained largely unchanged in high-SDI countries (555 in 2010 vs 554 in 2019; 0·2% reduction). The number and rate of adolescent deaths and DALYs owing to environmental heat and cold exposure increased for the high-SDI quintile during 2010–19. Interpretation: As other causes of mortality are addressed, inadequate progress in reducing transport and unintentional injury mortality as a proportion of adolescent deaths becomes apparent. The relative shift in the burden of injury from high-SDI countries to low and low–middle-SDI countries necessitates focused action, including global donor, government, and industry investment in injury prevention. The persisting burden of DALYs related to transport and unintentional injuries indicates a need to prioritise innovative measures for the primary prevention of adolescent injury. Funding: Bill & Melinda Gates Foundation. © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
Aluminium Oxide Thin-Film Based in Vitro Cell-Substrate Sensing Device for Monitoring Proliferation of Myoblast Cells
(Institute of Electrical and Electronics Engineers Inc., 2021) Uvanesh Kasiviswanathan; Chelladurai Karthikeyan Balavigneswaran; Chandan Kumar; Suruchi Poddar; Satyabrata Jit; Neeraj Sharma; Sanjeev Kumar Mahto
We demonstrate cell-substrate interaction on aluminium oxide thin-film in metal-insulator-metal structure followed by the change in dielectric characteristics of Al2O3 as a function of progression of cellular growth. The theoretical calculation of the fabricated biosensor reveals that the changes in the intrinsic elemental parameters are mainly attributed to the cell-induced behavioural changes. © 2002-2011 IEEE.
An improved xie-beni index for cluster validity measure
(Institute of Electrical and Electronics Engineers Inc., 2017) Munendra Singh; Romel Bhattacharjee; Neeraj Sharma; Ashish Verma
The pathology may appear as a new cluster(s) on radiological images and hence the information of cluster location cannot decide in prior. In this regard, the unsupervised methods of segmentation play the important role, however, these methods need the number of clusters as the input. The challenging tasks in clustering based image segmentation are to choose the number of segments in an image. This work proposes the segmentation quality index, which utilizes the trend of Xie-Beni index to obtain the optimum number of segments in an image. The proposed algorithm has been implemented on the segmentation results obtained by enhanced fuzzy c-means algorithm and compared with the classical validity indexes such as Xie-Beni index, partition entropy coefficient, partition coefficient and fuzzy hyper-volume on synthetic images and simulated brain MRI dataset images. The quantitative results show that the proposed method has greater ability to find the appropriate number of clusters on the ground truth and noisy images. © 2017 IEEE.
Automated medical image segmentation techniques
(2010) Neeraj Sharma; Amit K. Ray; K.K. Shukla; Shiru Sharma; Satyajit Pradhan; Arvind Srivastva; Lalit Aggarwal
Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.
Automatic segmentation of brain tumour in MR images using an enhanced deep learning approach
(Taylor and Francis Ltd., 2021) Sumit Tripathi; Ashish Verma; Neeraj Sharma
The presented manuscript proposes a fully automatic deep learning method to quantify the tumour region in brain Magnetic Resonance images as the accurate diagnosis of brain tumour region is necessary for the treatment of the patients. The irregular and confusing boundaries of tumours regions make it a challenging task to accurately figure out such regions. Another challenge with the segmentation task is of preserving the boundary details of the segmented tumour regions. The proposed network focuses on delineating the irregular tumour region as the best feature maps are learnt by the network, which is used for decoding; thus, it preserves the accurate boundary and pixel details. The proposed method incorporates internal residual connections in encoder and decoder to transfer feature maps directly to the successive layers to avoid loss of information contained in the images. The use of cross channel normalization (CCN) and parametric rectified linear unit (PRELU) gives a more balanced network output. The trained network produced remarkable results when tested on images of other datasets. Further, external clinical validation was performed by comparison of the algorithmic segmented images with those generated by a manual segmentation done by an experienced radiologist. We have termed our network as CCN-PR-Seg-net. © 2020 Informa UK Limited, trading as Taylor & Francis Group.
Bat optimization based neuron model of stochastic resonance for the enhancement of MR images
(PWN-Polish Scientific Publishers, 2017) Munendra Singh; Ashish Verma; Neeraj Sharma
Stochastic resonance (SR) performs the enhancement of the low in contrast image with the help of noise. The present paper proposes a modified neuron model based stochastic resonance approach applied for the enhancement of T1 weighted, T2 weighted, fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) sequences of magnetic resonance imaging. Multi objective bat algorithm has been applied to tune the parameters of the modified neuron model for the maximization of two competitive image performance indices contrast enhancement factor (F) and mean opinion score (MOS). The quality of processed image depends on the choice of these image performance indices rather the selection of SR parameters. The proposed approach performs well on enhancement of magnetic resonance (MR) images, as a result there is improvement in the gray-white matter differentiation and has been found helpful in the better diagnosis of MR images. © 2017 Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Classification of Gait Abnormalities Using Transfer Learning with EMG Scalogram Features
(Springer Science and Business Media Deutschland GmbH, 2023) Pranshu C. B. S. Negi; S.S. Pandey; Shiru Sharma; Neeraj Sharma
Gait abnormalities can significantly impact the mobility and quality of life of individuals, thus making its early diagnosis crucial for proper treatment planning and rehabilitation. In this study scalograms generated from EMG signals of two important gait abnormalities, rheumatoid arthritis, and prolapsed intervertebral disc are classified using transfer learning. Scalogram has an advantage when dealing with high-noise data with abrupt transitions making it an excellent choice for classifying movement patterns. When classified using only CNN an accuracy of 91.1%, precision of 92.8%, recall of 92.9%, AUC of 0.98, and a PRC of 0.97 were obtained. For transfer learning, VGG16, VGG19, ResNet50, Inceptionv3, InceptionResNet, MobileNet and MobileNetv2, and DenseNet Large were incorporated along with previous CNN. DenseNet Large achieved highest accuracy of 97.5% along with 96.2% precision, 96.2% recall, an AUC of 0.99 and a PRC of 0.99. The use of transfer learning provided a significant boost to performance of the model. The proposed method of using scalograms with transfer learning can be used to accurately diagnose gait abnormalities and allow medical professionals to design treatment and rehabilitation plan. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.
DEEP ENSEMBLE METHODS for IDENTIFICATION of MALICIOUS TISSUES in NOISY BREAST HISTOPATHOLOGICAL IMAGES
(World Scientific, 2025) Sumit Tripathi; Neeraj Sharma
This work addresses the issues of noise and tissue appearance fluctuations in histopathology image classification by using a novel deep ensemble method. The experiment's images were inherently noisy; however, the proposed approach includes features that allow for noise to be effectively encountered while classification tasks are being completed. This integration streamlines the categorization process by eliminating the requirement for a separate denoising phase. This approach encompasses studies on two types of noise, namely Gaussian and Rician, both commonly encountered in histopathological images. Remarkably, our proposed model demonstrated effectiveness in handling both types of noise, yielding satisfactory performance across diverse noise conditions. The proposed ensemble model achieves an accuracy of 83.74%, an F1-score of 81.72%, an F2-score of 81.04%, and an MCC of 83.99% for the highest level of rician noise. The proposed approach improves classification resilience and accuracy by combining the output of several deep-learning models. It does this by increasing the F2-score for malignant classes by 3-5%, which helps to reduce False Negatives. This approach differs from current technology and has promising implications for the diagnosis and treatment of breast cancer. Compared to other approaches, our suggested model performs better at higher noise levels. LIME and saliency map integration improve the interpretability of model decisions, which in turn improves classification accuracy and decision clarity. These features emphasize the adaptability and resilience of the suggested method, highlighting it as a potential instrument for enhancing the results of breast cancer diagnosis and therapy in clinical settings. The workload for pathologists is lessened, and diagnostic consistency and accuracy are improved through automation of the classification process. © 2025 National Taiwan University.
DEEP ENSEMBLE METHODS for IDENTIFICATION of MALICIOUS TISSUES in NOISY BREAST HISTOPATHOLOGICAL IMAGES
(World Scientific, 2024) Sumit Tripathi; Neeraj Sharma
This work addresses the issues of noise and tissue appearance fluctuations in histopathology image classification by using a novel deep ensemble method. The experiment's images were inherently noisy; however, the proposed approach includes features that allow for noise to be effectively encountered while classification tasks are being completed. This integration streamlines the categorization process by eliminating the requirement for a separate denoising phase. This approach encompasses studies on two types of noise, namely Gaussian and Rician, both commonly encountered in histopathological images. Remarkably, our proposed model demonstrated effectiveness in handling both types of noise, yielding satisfactory performance across diverse noise conditions. The proposed ensemble model achieves an accuracy of 83.74%, an F1-score of 81.72%, an F2-score of 81.04%, and an MCC of 83.99% for the highest level of rician noise. The proposed approach improves classification resilience and accuracy by combining the output of several deep-learning models. It does this by increasing the F2-score for malignant classes by 3-5%, which helps to reduce False Negatives. This approach differs from current technology and has promising implications for the diagnosis and treatment of breast cancer. Compared to other approaches, our suggested model performs better at higher noise levels. LIME and saliency map integration improve the interpretability of model decisions, which in turn improves classification accuracy and decision clarity. These features emphasize the adaptability and resilience of the suggested method, highlighting it as a potential instrument for enhancing the results of breast cancer diagnosis and therapy in clinical settings. The workload for pathologists is lessened, and diagnostic consistency and accuracy are improved through automation of the classification process. © 2024 National Taiwan University.
Deep learning-based automated multiclass classification of chest X-rays into Covid-19, normal, bacterial pneumonia and viral pneumonia
(Cogent OA, 2022) Alok Tiwari; Taresh Sarvesh Sharan; Shiru Sharma; Neeraj Sharma
Covid-19 has been a pandemic across almost all parts of the world. Due to its higher spread rate and increased mortality rate, early detection of this is required. In the present study, we have used chest X-Rays to identify the presence of Covid-19 and several other Pneumonia types (Viral and Bacterial). To perform this classification, we have used a transfer learning-based model relying upon a pre-trained VGG-16 classifier network. Along with that, we have used the inception module as a pre-processing cursor to this network. We present our model via three case study approaches, namely–Case (01)–four-class classification, Case (02)–three-class classification, and Case (03)–two-class classification. For these case studies, we have selected our classes from Normal, Covid-19, Viral Pneumonia, and Bacterial Pneumonia. We have evaluated our model’s classification performance on various parameters, such as—accuracy, precision, sensitivity, specificity, false-positive rate, and F1-score, as just one parameter is not sufficient enough to evaluate the performance. After training the network for all three cases, we have found Covid-19 classification accuracies–Case 01–91.86% (Four Classes), Case 02–97.67% (Three Classes), and Case 03–99.61% (Two Classes) and all the other parameters are well represented in the performance parameter section. Our proposed model testing accuracies for all three cases are–Case 01–87.32% (Four Classes), Case 02–96.89% (Three Classes), and Case 03–99.95% (Two Classes). Based on the achieved accuracies, our model showed comparable performance to pre-existing methods like VGG-16, Res-Net, and Inception-Net. We can use these case studies for the interpretation and classification of chest X-Rays in these classes and with increased dataset and computational power, we can apply the proposed model for more class classification. © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
Denoising of magnetic resonance images using discriminative learning-based deep convolutional neural network
(IOS Press BV, 2022) Sumit Tripathi; Neeraj Sharma
The noise in magnetic resonance (MR) images causes severe issues for medical diagnosis purposes. OBJECTIVE: In this paper, we propose a discriminative learning based convolutional neural network denoiser to denoise the MR image data contaminated with noise. METHODS: The proposed method incorporates the use of depthwise separable convolution along with local response normalization with modified hyperparameters and internal skip connections to denoise the contaminated MR images. Moreover, the addition of parametric RELU instead of normal conventional RELU in our proposed architecture gives more stable and fine results. The denoised images were further segmented to test the appropriateness of the results. The network is trained on one dataset and tested on other dataset produces remarkably good results. RESULTS: Our proposed network was used to denoise the images of different noise levels, and it yields better performance as compared with various networks. The SSIM and PSNR showed an average improvement of (7.2 ± 0.002) % and (8.5 ± 0.25) % respectively when tested on different datasets without retaining the network. An improvement of 5% and 6% was achieved in the values of mean intersection over union (mIoU) and BF score when the denoised images were segmented for testing the relevancy in biomedical imaging applications. The statistical test suggests that the obtained results are statistically significant as p< 0.05. CONCLUSION: The denoised images obtained are more clinically suitable for medical image diagnosis purposes, as depicted by the evaluation parameters. Further, external clinical validation was performed by an experienced radiologist for testing the validation of the resulting images. © 2022 - IOS Press. All rights reserved.
Detection of COVID-19 Infection in CT and X-ray images using transfer learning approach
(IOS Press BV, 2022) Alok Tiwari; Sumit Tripathi; Dinesh Chandra Pandey; Neeraj Sharma; Shiru Sharma
BACKGROUND: The infection caused by the SARS-CoV-2 (COVID-19) pandemic is a threat to human lives. An early and accurate diagnosis is necessary for treatment. OBJECTIVE: The study presents an efficient classification methodology for precise identification of infection caused by COVID-19 using CT and X-ray images. METHODS: The depthwise separable convolution-based model of MobileNet V2 was exploited for feature extraction. The features of infection were supplied to the SVM classifier for training which produced accurate classification results. RESULT: The accuracies for CT and X-ray images are 99.42% and 98.54% respectively. The MCC score was used to avoid any mislead caused by accuracy and F1 score as it is more mathematically balanced metric. The MCC scores obtained for CT and X-ray were 0.9852 and 0.9657, respectively. The Youden's index showed a significant improvement of more than 2% for both imaging techniques. CONCLUSION: The proposed transfer learning-based approach obtained the best results for all evaluation metrics and produced reliable results for the accurate identification of COVID-19 symptoms. This study can help in reducing the time in diagnosis of the infection. © 2022 - IOS Press. All rights reserved.
Dynamic Stochastic Resonance Based Diffusion-Weighted Magnetic Resonance Image Enhancement Using Multi-Objective Particle Swarm Optimization
(Springer Berlin Heidelberg, 2016) Munendra Singh; Neeraj Sharma; Ashish Verma; Shiru Sharma
Diffusion weighted (DW) magnetic resonance (MR) imaging maps the diffusion process of water in the tissues. DW-MR image is useful to probe the tissue microstructure, but suffers from inherent low signal to noise ratio and poor contrast. Dynamic stochastic resonance (DSR) utilizes the noise to enhance the low contrast image where the quality of the processed image depends on the bistability parameters of DSR and the number of iterations. This paper presents an approach that optimally finds the bistability parameters and number of iterations for the maximization of competitive image quality indices: contrast enhancement factor and mean opinion score using multi-objective particle swarm optimization. The proposed Particle Swarm Optimization optimized DSR algorithm has been tested on 40 DW-MR brain images of different subjects. The quantified results show average contrast enhancement factor, 1.603 and average perceptual quality measure, 9.508. These values are significantly higher than image quality indices of original image, the images that are produced by conventional enhancement methods and filtering followed by enhancement methods. © 2016, Taiwanese Society of Biomedical Engineering.
Effect of image transformations on dynamic stochastic resonance based MR image enhancement
(Institute of Electrical and Electronics Engineers Inc., 2017) Munendra Singh; Neeraj Sharma; Ashish Verma
Dynamic Stochastic Resonance (DSR) utilizes the noise associated with the image itself to enhance the image quality. This paper analyzes the effects of Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) of image, which works as the input to bi-stable nonlinear system exhibiting DSR. The study performed on T1, fluid-attenuated inversion recovery (FLAIR) and diffusion weighted sequences of Magnetic Resonance Imaging (MRI). The images were quantified in terms of contrast enhancement factor and image anisotropy. The results show that DSR based image enhancement is helpful to obtain better tissue differentiation. The DCT based DSR produces better enhancement for diffusion-weighted images whereas DWT and SVD based DSR produces better enhancement of T1 and FLAIR weighted magnetic resonance images. © 2017 IEEE.
EMG scalogram-based classification of gait disorders using attention-based CNN: a comparative study of wavelet functions
(Inderscience Publishers, 2024) Pranshu CBS Negi; Balendra; S.S. Pandey; Shiru Sharma; Neeraj Sharma
This study aims to classify gait abnormalities caused by rheumatoid arthritis and prolapsed intervertebral disc using scalograms from the EMG signals. Classifying EMG signals is difficult because of their variability, high dimensionality, and sensor placement. We propose to bridge this gap by using the wavelet transform and attention-based neural networks. The study involved five participants: one with rheumatoid arthritis, two with prolapsed intervertebral disc, and two healthy subjects. The proposed methodology uses four different wavelet functions: complex Gaussian, frequency B Spline, Mexican Hat, and Shannon, to construct scalograms, and an attention-based CNN for classification. A comparison of performance of the proposed algorithm with nine machine learning classifiers: K nearest neighbour, Naïve Bayes, support vector machine, decision tree, logistic regression, random forest, AdaBoost, gradient boost, and XGBoost was conducted. Out of the nine machine learning classifiers that were tested, XGBoost achieved the highest accuracy of 90.38%, however, in comparison to this the performance of the proposed algorithm was much better, with an accuracy of 99% and precision of 99%. These results indicate that this approach is highly effective in accurately categorising EMG signals. Copyright © 2024 Inderscience Enterprises Ltd.
Enhancement and Intensity Inhomogeneity Correction of Diffusion-Weighted MR Images of Neonatal and Infantile Brain Using Dynamic Stochastic Resonance
(Springer Berlin Heidelberg, 2017) Munendra Singh; Shiru Sharma; Ashish Verma; Neeraj Sharma
Imaging of infantile/neonatal brain mandates tailored radio frequency coils (RF coils) to achieve a homogeneous field over a small region of interest (ROI). Most centers, however, perform pediatric imaging using adult RF coils only as procurement of tailored pediatric coils might prove quite expensive. This practice may not scientifically justified, whereas the image post-processing techniques reduces the deleterious effects of magnetic field inhomogeneity due to a small ROI being scanned in a large RF coil. Further, the eccentric placement of ROI within the RF coil perpetuates the field inhomogeneity within the scanned region. Hence, the structures closer to the coil appear brighter than those farther away giving rise to a ‘shading artefact’. The effect even more accentuates in weak signal sequences like diffusion-weighted imaging (DWI). The proposed method significantly removes shading artefact of real DWI and synthetic T1 and T2 weighted magnetic resonance images. Dynamic stochastic resonance (DSR) intelligently uses the coefficient of discrete cosine transform of an image for brightness normalization and image enhancement simultaneously. The quality of the output image depends on the bistability parameters associated with the dynamic equation. Particle swarm optimization (PSO) tunes these bistability parameters for the entropy minimization of different group of tissues individually. The proposed algorithm outperforms the post processing based homomorphic filtering, local entropy minimization with spline model and multiplicative intrinsic component optimization methods. The proposed PSO based DSR approach may be helpful in accurate diagnosis. © 2017, Taiwanese Society of Biomedical Engineering.
Evaluation of single-photon emission computed tomography images obtained with and without copper filter by segmentation
(Medknow Publications, 2016) Subhash Chand Kheruka; Lalit Mohan Aggarwal; Neeraj Sharma; Umesh Chand Naithani; Anil Kumar Maurya; Sanjay Gambhir
Background: Measurement of accurate attenuation of photon flux in tissue is important to obtain reconstructed images using single-photon emission computed tomography (SPECT). Computed tomography (CT) scanner provides attenuation correction data for SPECT as well as anatomic information for diagnostic purposes. Segmentation is a process of dividing an image into regions having similar properties such as gray level, color, texture, brightness, and contrast. Image segmentation is an important tool for evaluation of medical images. X-ray beam used in CT scan is poly-energetic; therefore, we have used a copper filter to remove the low energy X-rays for obtaining correct attenuation factor. Images obtained with and without filters were quantitatively evaluated by segmentation method to avoid human error. Materials and Methods: Axial images of AAPM CT phantom were acquired with 3 mm copper filter (low intensity) and without copper filter (high intensity) using low-dose CT (140 kvp and 2.5 mA) of SPECT/CT system (Hawkeye, GE Healthcare). For segmentation Simulated Annealing Based Fuzzy c-means, algorithm is applied. Quantitative measurement of quality is done based on universal image quality index. Further, for the validation of attenuation correction map of filtered CT images, Jaszczak SPECT phantom was filled with 500 MBq of 99m Tc and SPECT study was acquired. Low dose CT images were acquired for attenuation correction to be used for reconstruction of SPECT images. Another set of CT images were acquired after applying additional 3 mm copper filter. Two sets of axial SPECT images were reconstructed using attenuation map from both the CT images obtained without and with a filter. Results and Conclusions: When we applied Simulated Annealing Based Fuzzy c-means segmentation on both the CT images, the CT images with filter shows remarkable improvement and all the six section of the spheres in the Jaszczak SPECT phantom were clearly visualized.