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Permanent URI for this collectionhttps://hdl.handle.net/11443/932

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Subject: search.filters.subject.deep learning

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    Predicting intra-operative and postoperative consequential events using machine-learning techniques in patients undergoing robot-assisted partial nephrectomy: a Vattikuti Collective Quality Initiative database study
    (WILEY, 2020-01-01) Bhandari, Mahendra; Nallabasannagari, Anubhav Reddy; Reddiboina, Madhu; Porter, James R.; Jeong, Wooju; Mottrie, Alexandre; Dasgupta, Prokar; Challacombe, Ben; Abaza, Ronney; Rha, Koon Ho; Parekh, Dipen J.; Ahlawat, Rajesh; Capitanio, Umberto; Yuvaraja, Thyavihally B.; Rawal, Sudhir; Moon, Daniel A.; Buffi, Nicolo M.; Sivaraman, Ananthakrishnan; Maes, Kris K.; Porpiglia, Francesco; Gautam, Gagan; Turkeri, Levent; Meyyazhgan, Kohul Raj; Patil, Preethi; Menon, Mani; Rogers, Craig
    Objective To predict intra-operative (IOEs) and postoperative events (POEs) consequential to the derailment of the ideal clinical course of patient recovery. Materials and Methods The Vattikuti Collective Quality Initiative is a multi-institutional dataset of patients who underwent robot-assisted partial nephectomy for kidney tumours. Machine-learning (ML) models were constructed to predict IOEs and POEs using logistic regression, random forest and neural networks. The models to predict IOEs used patient demographics and preoperative data. In addition to these, intra-operative data were used to predict POEs. Performance on the test dataset was assessed using area under the receiver-operating characteristic curve (AUC-ROC) and area under the precision-recall curve (PR-AUC). Results The rates of IOEs and POEs were 5.62\% and 20.98\%, respectively. Models for predicting IOEs were constructed using data from 1690 patients and 38 variables
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    Diagnostic Performance of AI for Cancers Registered in A Mammography Screening Program: A Retrospective Analysis
    (SAGE PUBLICATIONS INC, 2022-01-01) Kizildag Yirgin, Inci; Koyluoglu, Yilmaz Onat; Seker, Mustafa Ege; Ozkan Gurdal, Sibel; Ozaydin, Ayse Nilufer; Ozcinar, Beyza; Cabioglu, Neslihan; Ozmen, Vahit; Aribal, Erkin
    Purpose: To evaluate the performance of an artificial intelligence (AI) algorithm in a simulated screening setting and its effectiveness in detecting missed and interval cancers. Methods: Digital mammograms were collected from Bahcesehir Mammographic Screening Program which is the first organized, population-based, 10-year (2009-2019) screening program in Turkey. In total, 211 mammograms were extracted from the archive of the screening program in this retrospective study. One hundred ten of them were diagnosed as breast cancer (74 screen-detected, 27 interval, 9 missed), 101 of them were negative mammograms with a follow-up for at least 24 months. Cancer detection rates of radiologists in the screening program were compared with an AI system. Three different mammography assessment methods were used: (1) 2 radiologists' assessment at screening center, (2) AI assessment based on the established risk score threshold, (3) a hypothetical radiologist and AI team-up in which AI was considered to be the third reader. Results: Area under curve was 0.853 (95\% CI = 0.801-0.905) and the cut-off value for risk score was 34.5\% with a sensitivity of 72.8\% and a specificity of 88.3\% for AI cancer detection in ROC analysis. Cancer detection rates were 67.3\% for radiologists, 72.7\% for AI, and 83.6\% for radiologist and AI team-up. AI detected 72.7\% of all cancers on its own, of which 77.5\% were screen-detected, 15\% were interval cancers, and 7.5\% were missed cancers. Conclusion: AI may potentially enhance the capacity of breast cancer screening programs by increasing cancer detection rates and decreasing false-negative evaluations.
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    KEBOT: An Artificial Intelligence Based Comprehensive Analysis System for FUE Based Hair Transplantation
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020-01-01) Erdogan, Koray; Acun, Onur; Kucukmanisa, Ayhan; Duvar, Ramazan; Bayramoglu, Alp; Urhan, Oguzhan
    Robots and artificial intelligence technologies have become very important in the health applications as in many other fields. The proposed system in this work aims to provide detailed analysis of pre-op and post-op stage of FUE hair transplant procedures to enable surgeon to plan and assess success of the operations. In order to achieve this target, a robotic and vision-based system imaging and AI based analysis approach is developed. The proposed system performs analyses in three main stages: initialization, scanning, and analysis. At the initialization stage, 3D model of the patient's head generated at first by locating a depth camera in various positions around the patient by the help of a collaborative robot. At the second stage, where high resolution image capturing is performed in a loop with the usage of the 3D model, raw images are processed by a deep learning based object detection algorithm where follicles in pre-op and extracted follicle positions (i.e. holes) and placed grafts in post-op is detected. At the last stage, thickness of each hair is computed at the detected hair follicle positions using another deep learning-based segmentation approach. These data are combined to obtain objective evaluation criteria to generate patient report. Experimental results show that the developed system can be used successfully in hair transplantation operations.
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    Generating ad creatives using deep learning for search advertising
    (Scientific and Technological Research Council Turkey, 2022-01-01) Cogalmis, Kevser Nur; Bulut, Ahmet
    We generated advertisement creatives programmatically using deep neural networks. A landing page contains relevant text data, which can be used for generating advertisement creatives, i.e. ads. We treated the ad generation task as a text summarization problem and built a sequence to sequence model. In order to assess the validity of our approach, we conducted experiments on four datasets. Our empirical results showed that our model generated relevant ads on a template-based dataset with moderate hyperparameters. Training the model with more content increased the performance of the model, which we attributed to rigorous hyperparameter tune-up. The choice of word embedding used in the representation of the input altered the model's performance. When the source and the target shared common sequences during training, the model produced the best results.