Browsing by Author "Sohail, Ayesha"

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Application of Machine Learning Techniques To Analyze Anastomosis Integrity After Total Gastrectomy for Prediction of Clinical Leakage
(Springer Heidelberg, 2019) Celik, Sebahattin; Sohail, Ayesha; Ashraf, Shaina; Arshad, Arooba
Intraoperative testing (IT) is used to confirm the integrity of gastrointestinal anastomosis. Clinical trials are available in the literature to support the fact that methylene blue can identify the leaks, and can thus help in minimizing the postoperative ratio of clinical leaks after total gastrectomy. In the recent literature, machine learning tools have been used very successfully to investigate the hypothesis of such complex clinical trials, where incomplete data is available. In this article, data obtained from a clinical study, is analyzed using machine learning, to verify whether or not the methylene blue test can accurately identify the leaks and to predict future outcomes. Furthermore, a comparative study based on most robust machine learning solvers is presented in this article to identify the most appropriate machine learning technique(s) for future applications. We have considered the data (over a period starting from Jan 2007 till Dec 2014) based on the total gastrostomies (TG), where methylene blue test was applied. Data was obtained from 198 patients having gastric cancer. Out of 198, 108 cases went through methylene blue test done by a nasojejunal tube while no test was carried out for rest of 90 cases. Intraoperative leakage rate, mortality rate, length of hospitalization and postoperative clinical leakage rate were the measured outcomes. To analyze the data and to predict whether there will be a leak or not, machine learning techniques were applied and the accuracy was compared. The main objective of this research is to predict the clinical leakage after applying methylene blue test on gastric cancer patients. This objective is successfully achieved by implementing six machine learning approaches. Case specific machine learning approaches are discussed to evaluate post clinical leakage rate and radio leakage rate. From our analysis, we have concluded that the prediction of intraoperative leak, post clinical leak and radio leak is possible with the aid of different machine learning techniques. An important conclusion drawn from this study is that a single machine learning technique can not accurately predict different stages of leak, since the accuracy of the technique depends on the specification of clinical data that varies from stage to stage.
Benchmarking Coefficients for Forecasting Weight Loss After Sleeve Gastrectomy Biomedical Engineering
(World Scientific Publ Co Pte Ltd, 2020) Celik, Sebahattin; Sohail, Ayesha; Arif, Fatima; Ozdemir, Abdulselam
Background/Aim: In treatment practice of obesity, losing excess weight and then maintaining an ideal body weight are very important. By the sleeve gastrectomy initial weight loss is easier, but the progress of patients have diverse variability in terms of maintaining weight loss. Predicting models for weight changes may provide doctors and patients a good tool to modify their approach to obesity treatment.The main objective of this research is to verify the dependence of weight loss on sleeve coefficients and to forecast the weight loss. The weight loss and its dependence on remnant gastric volume compartmants (antral and body parts), after laparoscopic sleeve gastrectomy (LSG) is discussed in this paper. Data was obtained from a previous study which included 63 patients. Deep analysis of weight loss after LSG and its relation with remnant gastric volume is still a challenge due to weight loss dependence on multiple factors. During this research, with the aid of machine learning regression classifier, the relationship(s) between the sleeve coefficients' formulae and weight loss formulae (%EWL and %TWL), are developed in a novel way. Other factors such as age and gender are also taken into account. A robust approach of artificial intelligence, i.e. the "Neural Network Bayesian Regularization" is adopted to utilize the third month, sixth month and first year weight loss data, to forecast the second year weight loss. Models are proposed to demonstrate the dependance of total weight loss on crucial parameters of components of remanat gastric volumes. A comparative study is conducted for the appropriate selection of artificial intelligence training algorithm.
Lavaboda Akıllı Telefon Kullanımı ile Hemoroidal Hastalık Arasındaki İlişki
(2021) Çağlıkülekçi, Mehmet; Zarbaliyev, Elbrus; Özdemir, Abdulselam; Sohail, Ayesha; Çelik, Sebahattin
Amaç: Hemoroidal hastalık, nüfusun önemli bir bölümünün bir noktada karşılaştığı proktolojik bir sorundur. Günümüzde, cep telefonlarının aşırı sosyal medya alışkanlıkları ile birlikte aşırı kullanımı, birincisinin tuvalette kullanılmasına yol açmıştır. Bu çalışmanın amacı, lavaboda cep telefonu kullanımının hemoroidal hastalık ile ilişkili olup olmadığını araştırmaktır. Yöntem: Genel cerrahi polikliniğine hemoroid şikayeti ile başvuran hastalar çalışma grubuna dahil edilirken, bu tür şikayeti olmayan sağlıklı gönüllüler kontrol grubunu oluşturdu. Tüm katılımcılara cep telefonu kullanım alışkanlıkları hakkında sorular içeren bir anket verildi. Hemoroid hastalığının derecesi, varsa, fizik muayeneleri bizzat yapan deneyimli bir genel cerrah tarafından belirlendi. Bulgular: Çalışma grubu 882 katılımcı ve 802 kontrol grubundan oluşmaktaydı. Birincisinin %64,7’si (571 hasta) yanlarında cep telefonlarını lavaboya götürürken, bu oran kontrol grubu için sadece %38,4 (308 hasta) idi (p<0,001). Çalışma grubunun %49,9’u tuvaletteyken cep telefonlarında zaman geçirirken, kontrol grubunun sadece %27,3’ü bunu yapmıştı (p<0,001). Lavaboda bir cep telefonu kullanılarak geçirilen her ilave dakika için, hemoroit grubunda olma olasılığının 1,26 kat arttığı belirlendi (%95 güven aralığı =1,162-1,364). Sonuç: Cep telefonları artık lavaboda bile yaygın olarak kullanılmaktadır, bu alışkanlık hemoroit için risk faktörü olabilir. Bu nedenle, hemoroid tedavisi için, hastaların bu alışkanlığı bırakmaları tavsiye edilebilir.
Neural Networks To Understand the Physics of Oncological Medical Imaging
(World Scientific Publ Co Pte Ltd, 2022) Al-Utaibi, Khaled A.; Sohail, Ayesha; Arif, Fatima; Celik, S.; Sait, Sadiq M.; Keskin, Derya Bako
The evolving field of computational image analysis has its applications in the industry, manufacturing and biological sciences, especially in the field of medical imaging. Medical imaging and computational physics have evolved together during the past decades with the advancement in the field of artificial intelligence (AI). Deep learning is the sub-domain of AI that mostly deals with imaging data for classification, segmentation and reconstruction. The time series of medical images of different patients, with different staging are categorized based on the physical and biological consequences. The hypothesis of the current research is that the deep learning tool, if trained on several patients, can identify the stage of cancer swiftly for fresh data sets. During this research, an advance Convolutional Neural Network (CNN) strategy is adopted to classify the cancer stage for a group of patients of gastric cancer. The CNN model makes use of skipping connections for better prediction. CNNs have been quite popular in medical imaging for their ability of feature detection. CNNs are used in the recent literature for the analysis of images. During this research, we have used the state-of-the-art Matlab ResNet CNN toolbox for the analysis of the images obtained from esophageal and gastric cancer patients. It was concluded that RESNET50 is a reliable algorithm for the determination of tumor mass on CT Scans. Moreover, the performance of the model can be improved by giving a comparatively larger data set as an input to the model. Inspired from Caltech101, a logic related to RESNET50 was adopted. The data was processed and an algorithm was designed to develop a mapping, based on the mass of tumor. The algorithm designed successfully identified the images, randomly picked from different patients, based on the image features.
Physics of Fractional Imaging in Biomedicine
(Pergamon-elsevier Science Ltd, 2018) Sohail, Ayesha; Beg, O. A.; Li, Zhiwu; Celik, Sebahattin
The mathematics of imaging is a growing field of research and is evolving rapidly parallel to evolution in the field of imaging. Imaging, which is a sub-field of biomedical engineering, considers novel approaches to visualize biological tissues with the general goal of improving health. "Medical imaging research provides improved diagnostic tools in clinical settings and supports the development of drugs and other therapies. The data acquisition and diagnostic interpretation with minimum error are the important technical aspects of medical imaging. The image quality and resolution are really important in portraying the internal aspects of patient's body. Although there are several user friendly resources for processing image features, such as enhancement, colour manipulation and compression, the development of new processing methods is still worthy of efforts. In this article we aim to present the role of fractional calculus in imaging with the aid of practical examples. (C) 2018 Elsevier Ltd. All rights reserved.