Artificial intelligence assisted patient blood and urine droplet pattern analysis for non-invasive and accurate diagnosis of bladder cancer

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Demir, Ramiz and Koc, Soner and Ozturk, Deniz Gulfem and Bilir, Şükriye and Ozata, Halil İbrahim and Williams, Rhodri and Christy, John and Akkoc, Yunus and Tinay, İlker and Gunduz-Demir, Cigdem and Gözüaçık, Devrim (2024) Artificial intelligence assisted patient blood and urine droplet pattern analysis for non-invasive and accurate diagnosis of bladder cancer. Scientific Reports, 14 (1). ISSN 2045-2322

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Abstract

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.
Item Type: Article
Divisions: Faculty of Engineering and Natural Sciences
Sabancı University Nanotechnology Research and Application Center
Depositing User: Devrim Gözüaçık
Date Deposited: 08 Jun 2024 16:22
Last Modified: 08 Jun 2024 16:22
URI: https://research.sabanciuniv.edu/id/eprint/49075

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