The application of artificial intelligence in the detection of basal cell carcinoma: A systematic review

Abstract

Basal cell carcinoma (BCC) is one of the most common forms of cancer, 1-3 with a rising incidence worldwide. To date, histopathological examination of a punch biopsy is the gold standard to distinguish BCC from alternative diagnoses and to determine the BCC subtype. 1,4 However, a punch biopsy is an invasive procedure, with risks of pain and bleeding during the procedure and the additional chance of infection and/or scarring. 5 Moreover, awaiting histopathological assessment may be stressful for many patients. Considering the rising incidence of BCC, which causes a major burden on healthcare systems, finding alternatives to an invasive biopsy is desired.The last century has seen many advances in medical imaging from the first X-ray to more sophisticated imaging techniques, such as computed tomography (CT) and magnetic resonance imaging (MRI). 6 This progress, concurrent with advances in computational power and machine learning techniques, often termed artificial intelligence (AI) in the literature, eased the transformation of medical images into quantitative minable data that could be used to build diagnostic, predictive and prognostic clinical decision support systems (cDSS). 7,8 Quantitative imaging analysis techniques that are being extensively investigated to develop cDSS include handcrafted radiomics and deep learning. Handcrafted radiomic features

The Past, Present, Future: Pathophysiology, Diagnosis, and Treatment of Human Skin Diseases

“…Open-access databases like the Human Against Machine (HAM10000) and International Skin Imaging Collaboration are undeniably valuable, yet open-source databases can house data that is poorly labeled, organized, or processed. Additionally, complex data formats for imaging like OCT have been around for years yet the proportion of open-source databases remains much lower than that for images along These challenges are compounded by a lack of standardization in image storage and sharing and potential biases due to over-representation of certain skin conditions and inadequate diversity in skin tones, limiting the applicability of AI models across different patient demographics [102,103].…”

3D reconstruction and artificial intelligence algorithms for the detection and tumor depth measurement of basal cell carcinoma in RCM-OCT images: a pilot study

“…Artificial intelligence (AI) can potentially help readers detect BCCs and measure the tumor depth in these images. Previous studies have applied AI to dermoscopy, RCM, or OCT images to diagnose BCC non-invasively 5,6 . AI detection of BCC from polarization-sensitive OCT (PS-OCT) images has a reported 95% sensitivity and 95% specificity 7 .…”

Are we Capable of Contributing to the Reduction of Eyelid Basal Cell Carcinoma? Building a Healthcare Team

“…Skin tumours are the most prevalent in the world [5,6]. among these, malignant eyelid tumours are mostly represented by BCC and squamous cell carcinoma (SCC) [3,7].…”