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

• Published in: Scientific reports Vol. 14; no. 1; pp. 2488 - 10
• Main Authors: Demir, Ramiz, Koc, Soner, Ozturk, Deniz Gulfem, Bilir, Sukriye, Ozata, Halil İbrahim, Williams, Rhodri, Christy, John, Akkoc, Yunus, Tinay, İlker, Gunduz-Demir, Cigdem, Gozuacik, Devrim
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 30.01.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/67; Artificial intelligence; Biopsy; Bladder cancer; Blood; Diagnosis; Humanities and Social Sciences; Invasiveness; Malignancy; Medical diagnosis; multidisciplinary; Neural networks; Science; Science (multidisciplinary); Urine
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-52728-7

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.