Artificial intelligence systems in dental shade-matching: A systematic review.

PURPOSE: Uses for artificial intelligence (AI) are being explored in contemporary dentistry, but artificial intelligence in dental shade-matching has not been systematically reviewed and evaluated. The purpose of this systematic review was to evaluate the accuracy of artificial intelligence in predicting dental shades in restorative dentistry. METHODS: A systematic electronic search was performed with the databases MEDLINE (PubMed), Scopus, Cochrane Library, and Google Scholar. A manual search was also conducted. All titles and abstracts were subject to the inclusion criteria of observational, interventional studies, and studies published in the English language. Narrative reviews, systematic reviews, case reports, case series, letters to the editor, commentaries, studies that were not AI-based, studies that were not related to dentistry, and studies that were related to other disciplines in dentistry, other than restorative dentistry (prosthodontics and endodontics) were excluded. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute Critical Appraisal Checklist for Quasi-Experimental Studies (non-randomized experimental studies). A third investigator was consulted to resolve the lack of consensus. RESULTS: Fifty-three articles were initially found from all the searches combined from articles published from 2008 till March 2023. A total of 15 articles met the inclusion criteria and were included in the systematic review. AI algorithms for shade-matching include fuzzy logic, a genetic algorithm with back-propagation neural network, back-propagation neural networks, convolutional neural networks, artificial neural networks, support vector machine algorithms, K-nearest neighbor with decision tree and random forest, deep learning for detection of dental prostheses based on object-detection applications, You Only Look Once-YOLO. Moment invariant was used for feature extraction.  XG (Xtreme Gradient) Boost was used in one study as a gradient-boosting machine learning algorithm. The highest accuracy in the prediction of dental shades was the decision tree regression model for leucite-based dental ceramics of 99.7% followed by the fuzzy decision of 99.62%, and support vector machine using cross-validation of 97%. CONCLUSIONS: Lighting conditions, shade-matching devices and color space models, and the type of AI algorithm influence the accuracy of the prediction of dental shades. Knowledge-based systems and neural networks have shown better accuracy in predicting dental shades.

Cytogenetics in Oral Cancer: A Comprehensive Update.

AIM: To evaluate the application of cytogenetic techniques in determining the diagnosis, prognosis, and therapeutics in oral cancer. BACKGROUND: Genetic aberrations that play an important role in oral oncogenesis demand substantial research for in-depth characterization of the tumor. Cytogenetic techniques have the potential to detect these aberrations. This review highlights about various cytogenetic approaches in cancer and how these findings support its application in the field of oral oncology. METHODS: Google scholar search was done for articles on cancer cytogenetics, and in particular, PubMed database was queried for articles published from 2015 to 2020 using keywords cytogenetics, chromosomal aberrations, conventional cytogenetics, karyotyping, banding techniques, molecular cytogenetics, fluorescent in situ hybridization, spectral karyotyping, comparative genomic hybridization, multiplex ligation probe analysis, and next-generation sequencing (NGS) in oral cancer. Abstracts were reviewed, and relevant full text was accessed to extract the cytogenetic findings in oral cancer. RESULTS: Data regarding various cytogenetic approaches from conventional to molecular techniques have been published in oral cancer. They convey a highly complex cytogenetic finding from gross chromosomal aberrations to specific gene mutations in oral cancer. CONCLUSION: Crucial information in the development and progression of oral cancer is achieved through cytogenetic findings in particular with the molecular cytogenetic techniques. Novel technologies like NGS have emerged in recent years that hold promise in the detection of these alterations more efficiently. CLINICAL SIGNIFICANCE: An appraisal of cytogenetic analysis in oral cancer helps to determine the diagnosis and the most important prognosticators. It assists in building targeted therapies for patient benefit.

Procedures and pitfalls in incisional biopsies of oral squamous cell carcinoma with respect to histopathological diagnosis.

This review has addressed the issues faced by a diagnostic pathologist during routine assessment of haematoxylin and eosin stained incisional biopsies from oral squamous cell carcinoma patients. Herein, the pragmatic means undertaken, has highlighted routinely faced problems & encounters determined at various levels as clinical, laboratory and diagnostic pitfalls, when possible, tips offered towards procedures and guidance. Also, dealt with subtypes of oral squamous cell carcinoma, differential diagnosis, and relevant prognostic indicators that can navigate the surgeon to take quick decisions. It speaks of the journey of biopsied material from the clinician to the laboratory until the generation of the final report. Although histopathological evaluation is a confirmatory tool for any clinically suspected lesion it mandates the co-operation of faculty from varied disciplines. The onus lies on a pathologist to establish standard protocols to oversee, audit the laboratory operating procedures from time and again. Technical errors and faults at the office desk doesn't come under the purview of this review.