Deep-learning-enabled automatic gene abnormality detection via fluorescence in situ hybridization

Fluorescence in situ hybridization (FISH) is widely used for diagnosing cancer and genetic disorders due to its high specificity and accuracy. However, traditional methods face challenges such as suboptimal focus adjustments, subjective signal counting errors, and inefficiencies in imaging, limiting their use in high-throughput screening. To address these issues, we introduced the integrated FISH imaging and analysis system (FAST), an innovative solution that combines rapid filter switching, automated focusing, multilayer fluorescence signal fusion, and the improved ResNet152 deep learning framework. Compared with clinical manual counts and analysis of case reports of 10 patients with chronic lymphocytic leukemia (CLL), the FAST achieved an average nuclei segmentation accuracy of 98.28%. For abnormal gene detection, the model achieved an accuracy of 97.86%. Additionally, its intuitive interface allows the operator to complete the entire workflow—from scanning to report generation—within 45 min. FAST represents a significant advancement in cancer and genetic disorder diagnostics, offering a powerful tool for early detection.