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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3

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Department: search.filters.department.Department of Computer EngineeringSubject: search.filters.subject.Multidisciplinary sciences

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    Artificial intelligence assisted patient blood and urine droplet pattern analysis for non-invasive and accurate diagnosis of bladder cancer
    (Nature Portfolio, 2024) 0000-0001-6435-7883; N/A; N/A; 0000-0001-6749-8518; 0000-0001-5379-6151; 0000-0003-0724-1942; 0000-0001-7739-2346; Bilir, Sukriye; Williams, Rhodri; Christy, John; Tinay, Ilker; N/A; N/A; N/A; N/A; N/A; Department of Computer Engineering; N/A; Demir, Ramiz; Koç, Soner; Öztürk, Deniz Gülfem; Özata, İbrahim Halil; Akkoç, Yunus; Demir, Çiğdem Gündüz; Gözüaçık, Devrim; PhD Student; PhD Student; Researcher; Teaching Faculty; Researcher; Faculty Member; Faculty Member; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); Graduate School of Health Sciences; Graduate School of Health Sciences; N/A; School of Medicine; N/A; College of Engineering; School of Medicine; N/A; N/A; N/A; 177151; N/A; 43402; 40248
    Bladder cancer is one of the most common cancer types in the urinary system. Yet, current bladder cancer diagnosis and follow-up techniques are time-consuming, expensive, and invasive. In the clinical practice, the gold standard for diagnosis remains invasive biopsy followed by histopathological analysis. In recent years, costly diagnostic tests involving the use of bladder cancer biomarkers have been developed, however these tests have high false-positive and false-negative rates limiting their reliability. Hence, there is an urgent need for the development of cost-effective, and non-invasive novel diagnosis methods. To address this gap, here we propose a quick, cheap, and reliable diagnostic method. Our approach relies on an artificial intelligence (AI) model to analyze droplet patterns of blood and urine samples obtained from patients and comparing them to cancer-free control subjects. The AI-assisted model in this study uses a deep neural network, a ResNet network, pre-trained on ImageNet datasets. Recognition and classification of complex patterns formed by dried urine or blood droplets under different conditions resulted in cancer diagnosis with a high specificity and sensitivity. Our approach can be systematically applied across droplets, enabling comparisons to reveal shared spatial behaviors and underlying morphological patterns. Our results support the fact that AI-based models have a great potential for non-invasive and accurate diagnosis of malignancies, including bladder cancer.
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    Forecasting daily COVID-19 case counts using aggregate mobility statistics
    (MDPI, 2022) Department of Computer Engineering; Department of Computer Engineering; Department of Computer Engineering; Boru, Bulut; Gürsoy, Mehmet Emre; Undergraduate Student; Faculty Member; College of Engineering; College of Engineering; N/A; 330368
    The COVID-19 pandemic has impacted the whole world profoundly. For managing the pandemic, the ability to forecast daily COVID-19 case counts would bring considerable benefit to governments and policymakers. In this paper, we propose to leverage aggregate mobility statistics collected from Google's Community Mobility Reports (CMRs) toward forecasting future COVID-19 case counts. We utilize features derived from the amount of daily activity in different location categories such as transit stations versus residential areas based on the time series in CMRs, as well as historical COVID-19 daily case and test counts, in forecasting future cases. Our method trains optimized regression models for different countries based on dynamic and data-driven selection of the feature set, regression type, and time period that best fit the country under consideration. The accuracy of our method is evaluated on 13 countries with diverse characteristics. Results show that our method's forecasts are highly accurate when compared to the real COVID-19 case counts. Furthermore, visual analysis shows that the peaks, plateaus and general trends in case counts are also correctly predicted by our method.