Evaluation of Artificial Intelligence Algorithms Performance in the Histopathological Diagnosis of Seborrheic Keratosis

Seborrheic keratosis (SK) is one of the most common benign epidermal tumors. Treatment is generally unnecessary, although lesions may be removed because of irritation, pruritus, or cosmetic concerns. SK has several clinical and histological subtypes, including common seborrheic keratosis (CSK), which is more prevalent among Caucasians, and dermatosis papulosa nigra (DPN), which is more common in individuals with Fitzpatrick skin phototypes III and above. Its development is associated mainly with age and genetic predisposition, with possible contribution from ultraviolet radiation. Lesions may occur almost anywhere except the palms and soles, with the face and upper trunk being common sites. Diagnosis is usually clinical but may be supported by dermoscopy or histopathology.

Histologically, SK represents an intraepidermal proliferation of squamous or basaloid cells. The characteristic findings include acanthosis, papillomatosis, hyperkeratosis, keratin cysts, and keratin pseudocysts. Cellular atypia is generally absent, while the amount of melanin and melanocytes varies according to the degree of pigmentation. SK shows considerable clinical and histological variability, which can sometimes make differentiation from other lesions, such as keratoacanthoma and clear cell acanthoma, challenging.

Recent advances in digital pathology and artificial intelligence (AI) have created new opportunities for diagnostic support in dermatopathology. AI aims to mimic aspects of human intelligence, while machine learning enables computers to identify patterns from data. Deep learning, neural networks, and convolutional neural networks (CNNs) are particularly important in image analysis. AI in dermatopathology has evolved from early text-based systems such as TEGUMENT, introduced in 1987, to modern systems capable of directly analyzing digital pathology images. Although widespread clinical implementation is still developing, AI has potential applications in diagnosis, triage, education, and research and requires collaboration between dermatopathologists, pathologists, clinicians, engineers, and data scientists.

Study Methodology

H&E-stained glass slides will be collected from the Al-Hussein dermatopathology archive, covering the period from 2010 to 2019. A panel of certified dermatopathologists will evaluate the slides. Their diagnoses will serve as the gold standard against which the AI results will be compared.