Accuracy of artificial intelligence-assisted detection of Oral Squamous Cell Carcinoma: A systematic review and meta-analysis. (October 2022)

• Record Type: Journal Article
• Authors: Elmakaty, Ibrahim; Elmarasi, Mohamed; Amarah, Ahmed; Abdo, Ruba; Malki, Mohammed Imad
• Is Part Of: Critical reviews in oncology/hematology. Volume 178(2022)
• Publication Date: 2022-10
• Subjects: Diagnostic accuracy -- Artificial intelligence -- Oral cancers -- Squamous Cell Carcinoma -- Meta-analysis; Oncology -- Periodicals; Hematology -- Periodicals; 616.994
• DOI: 10.1016/j.critrevonc.2022.103777
• Languages: English
• ISSNs: 1040-8428

Abstract

Abstract: Oral Squamous Cell Carcinoma (OSCC) is an aggressive tumor with a poor prognosis. Accurate and timely diagnosis is therefore essential for reducing the burden of advanced disease and improving outcomes. In this meta-analysis, we evaluated the accuracy of artificial intelligence (AI)-assisted technologies in detecting OSCC. We included studies that validated any diagnostic modality that used AI to detect OSCC. A search was performed in six databases: PubMed, Embase, Scopus, Cochrane Library, ProQuest, and Web of Science up to 15 Mar 2022. The Quality Assessment Tool for Diagnostic Accuracy Studies was used to evaluate the included studies' quality, while the Split Component Synthesis method was utilized to quantitatively synthesize the pooled diagnostic efficacy estimates. We considered 16 out of the 566 yielded studies, which included twelve different AI models with a total of 6606 samples. The summary sensitivity, summary specificity, positive and negative likelihood ratios as well as the pooled diagnostic odds ratio were 92.0 % (95 % confidence interval [CI] 86.7–95.4 %), 91.9 % (95 % CI 86.5–95.3 %), 11.4 (95 % CI 6.74–19.2), 0.087 (95 % CI 0.051–0.146) and 132 (95 % CI 62.6–277), respectively. Our findings support the capability of AI-assisted systems to detect OSCC with high accuracy, potentially aiding the histopathological examination in early diagnosis, yet more prospective studies are needed to justify their use in the real population. Highlights: Early and accurate diagnosis is essential in OSCC to reduce the disease burden. AI-assisted systems are rapid, non-invasive and demonstrated remarkable performance in detecting OSCC. Using AI can potentially aid the gold standard histopathological examination in the early diagnosis of OSCC. AI was also used in the resection, segmentation, metastasis prediction and treatment selection of OSCC. More prospective studies are needed to confirm AI diagnostic accuracy in clinical settings.