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All Journal International Journal of Advanced Health Science and Technology
Salma Abdel Wahed
Faculty of Medicine, Hashemite University, Zarqa, Jordan
Electrical & Electronics Engineering Health Professions Nursing Public Health

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Automated Detection of Histological Hallmarks in Frontotemporal Lobar Degeneration Using Deep Learning Salma Abdel Wahed; Mutaz Abdel Wahed
International Journal of Advanced Health Science and Technology Vol. 5 No. 3 (2025): June
Publisher : Forum Ilmiah Teknologi dan Ilmu Kesehatan (FORITIKES)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ad2r8e87

Abstract

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.
Co-Authors Mutaz Abdel Wahed