Araştırma Çıktıları

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    Multicenter Multireader Evaluation of an Artificial Intelligence-Based Attention Mapping System for the Detection of Prostate Cancer With Multiparametric MRI
    (AMER ROENTGEN RAY SOC, 2020-01-01) Mehralivand, Sherif; Harmon, Stephanie A.; Shih, Joanna H.; Smith, Clayton P.; Lay, Nathan; Argun, Burak; Bednarova, Sandra; Baroni, Ronaldo Hueb; Canda, Abdullah Erdem; Ercan, Karabekir; Girometti, Rossano; Karaarslan, Ercan; Kural, Ali Riza; Pursyko, Andrei S.; Rais-Bahrami, Soroush; Tonso, Victor Martins; Magi-Galluzzi, Cristina; Gordetsky, Jennifer B.; Silvestre e Silva Macarenco, Ricardo; Merino, Maria J.; Gumuskaya, Berrak; Saglican, Yesim; Sioletic, Stefano; Warren, Anne Y.; Barrett, Tristan; Bittencourt, Leonardo; Coskun, Mehmet; Knauss, Chris; Law, Yan Mee; Malayeri, Ashkan A.; Margolis, Daniel J.; Marko, Jamie; Yakar, Derya; Wood, Bradford J.; Pinto, Peter A.; Choyke, Peter L.; Summers, Ronald M.; Turkbey, Baris
    OBJECTIVE. The purpose of this study was to evaluate in a multicenter dataset the performance of an artificial intelligence (AI) detection system with attention mapping compared with multiparametric MRI (mpMRI) interpretation in the detection of prostate cancer. MATERIALS AND METHODS. MRI examinations from five institutions were included in this study and were evaluated by nine readers. In the first round, readers evaluated mpMRI studies using the Prostate Imaging Reporting and Data System version 2. After 4 weeks, images were again presented to readers along with the AI-based detection system output. Readers accepted or rejected lesions within four AI-generated attention map boxes. Additional lesions outside of boxes were excluded from detection and categorization. The performances of readers using the mpMRI-only and AI-assisted approaches were compared. RESULTS. The study population included 152 case patients and 84 control patients with 274 pathologically proven cancer lesions. The lesion-based AUC was 74.9\% for MRI and 77.5\% for AI with no significant difference (p = 0.095). The sensitivity for overall detection of cancer lesions was higher for AI than for mpMRI but did not reach statistical significance (57.4\% vs 53.6\%, p = 0.073). However, for transition zone lesions, sensitivity was higher for AI than for MRI (61.8\% vs 50.8\%, p = 0.001). Reading time was longer for AI than for MRI (4.66 vs 4.03 minutes, p < 0.001). There was moderate interreader agreement for AI and MRI with no significant difference (58.7\% vs 58.5\%, p = 0.966). CONCLUSION. Overall sensitivity was only minimally improved by use of the AI system. Significant improvement was achieved, however, in the detection of transition zone lesions with use of the AI system at the cost of a mean of 40 seconds of additional reading time.
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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.