Automated Detection of Histological Hallmarks in Frontotemporal Lobar Degeneration Using Deep Learning

Salma Abdel Wahed (Faculty of Medicine, Hashemite University, Zarqa, Jordan)
Mutaz Abdel Wahed (Computer Networks and Cybersecurity, Jadara University, Irbid, Jordan)

International Journal of Advanced Health Science and Technology
Vol. 5 No. 3 (2025): June

Frontotemporal lobar degeneration (FTLD) is a progressive neurodegenerative disease marked by distinct histological hallmarks, including Pick bodies. Manual identification is time-consuming, subjective, and requires expert neuropathologists. This study developed a convolutional neural network (CNN) for the automated detection of Pick bodies in histological images of FTLD. The model achieved 86.3% accuracy, 89.0% recall, and 0.91 ROC AUC, demonstrating its potential for objective and scalable identification of FTLD-related histopathological features, with applications for clinical diagnosis. Inference time per image was 0.042 seconds. Pixel density analysis revealed a significant difference between positive (mean 59.8) and negative (mean 47.3) regions. These findings support the feasibility of deep learning in neuropathology, enabling objective and scalable identification of FTLD-related changes. This approach offers potential for clinical integration, accelerated diagnosis, and expansion to other neurodegenerative disorders.