• MeSH
• lékařská informatika MeSH
• Publikační typ
• učebnice MeSH

• Konspekt
• Lékařské vědy. Lékařství
• NLK Obory
• lékařská informatika

PURPOSE: Fuchs endothelial corneal dystrophy (FECD) is a common, age-related cause of visual impairment. This systematic review synthesizes evidence from the literature on artificial intelligence (AI) models developed for the diagnosis and management of FECD. METHODS: We conducted a systematic literature search in MEDLINE, PubMed, Web of Science, and Scopus from January 1, 2000, to June 31, 2024. Full-text studies utilizing AI for various clinical contexts of FECD management were included. Data extraction covered model development, predicted outcomes, validation, and model performance metrics. We graded the included studies using the Quality Assessment of Diagnostic Accuracies Studies 2 tool. This review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations. RESULTS: Nineteen studies were analyzed. Primary AI algorithms applied in FECD diagnosis and management included neural network architectures specialized for computer vision, utilized on confocal or specular microscopy images, or anterior segment optical coherence tomography images. AI was employed in diverse clinical contexts, such as assessing corneal endothelium and edema and predicting post-corneal transplantation graft detachment and survival. Despite many studies reporting promising model performance, a notable limitation was that only three studies performed external validation. Bias introduced by patient selection processes and experimental designs was evident in the included studies. CONCLUSIONS: Despite the potential of AI algorithms to enhance FECD diagnosis and prognostication, further work is required to evaluate their real-world applicability and clinical utility. TRANSLATIONAL RELEVANCE: This review offers critical insights for researchers, clinicians, and policymakers, aiding their understanding of existing AI research in FECD management and guiding future health service strategies.

• MeSH
• Fuchsova endoteliální dystrofie * diagnóza   terapie   MeSH
• lidé MeSH
• optická koherentní tomografie metody   MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• systematický přehled MeSH

• MeSH
• lékařská informatika MeSH
• Publikační typ
• kongresy MeSH
• sborníky MeSH

• Konspekt
• Lékařské vědy. Lékařství
• NLK Obory
• lékařská informatika

OBJECTIVES: Artificial Intelligence (AI), particularly deep learning, has significantly impacted healthcare, including dentistry, by improving diagnostics, treatment planning, and prognosis prediction. This systematic mapping review explores the current applications of deep learning in dentistry, offering a comprehensive overview of trends, models, and their clinical significance. MATERIALS AND METHODS: Following a structured methodology, relevant studies published from January 2012 to September 2023 were identified through database searches in PubMed, Scopus, and Embase. Key data, including clinical purpose, deep learning tasks, model architectures, and data modalities, were extracted for qualitative synthesis. RESULTS: From 21,242 screened studies, 1,007 were included. Of these, 63.5% targeted diagnostic tasks, primarily with convolutional neural networks (CNNs). Classification (43.7%) and segmentation (22.9%) were the main methods, and imaging data-such as cone-beam computed tomography and orthopantomograms-were used in 84.4% of cases. Most studies (95.2%) applied fully supervised learning, emphasizing the need for annotated data. Pathology (21.5%), radiology (17.5%), and orthodontics (10.2%) were prominent fields, with 24.9% of studies relating to more than one specialty. CONCLUSION: This review explores the advancements in deep learning in dentistry, particulary for diagnostics, and identifies areas for further improvement. While CNNs have been used successfully, it is essential to explore emerging model architectures, learning approaches, and ways to obtain diverse and reliable data. Furthermore, fostering trust among all stakeholders by advancing explainable AI and addressing ethical considerations is crucial for transitioning AI from research to clinical practice. CLINICAL RELEVANCE: This review offers a comprehensive overview of a decade of deep learning in dentistry, showcasing its significant growth in recent years. By mapping its key applications and identifying research trends, it provides a valuable guide for future studies and highlights emerging opportunities for advancing AI-driven dental care.

• MeSH
• deep learning * MeSH
• lidé MeSH
• zubní lékařství * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH
• systematický přehled MeSH

• MeSH
• chorobopisy - počítačové systémy MeSH
• management znalostí MeSH
• metody pro podporu rozhodování MeSH
• počítačové zpracování obrazu MeSH
• počítačové zpracování signálu MeSH
• řízení zdravotnictví MeSH
• studium lékařství MeSH
• zdravotnické informační systémy MeSH
• Publikační typ
• sborníky MeSH

• Konspekt
• Lékařské vědy. Lékařství
• NLK Obory
• lékařská informatika
• NLK Publikační typ
• ročenky

• MeSH
• biologie buňky MeSH
• molekulární biologie MeSH
• Publikační typ
• monografie MeSH

• Konspekt
• Biochemie. Molekulární biologie. Biofyzika
• NLK Obory
• biologie
• cytologie, klinická cytologie