Key result
Deep-learning models achieve up to ~96% diagnostic accuracy for coeliac disease using endoscopic images.
Why the study?
Coeliac disease diagnosis often relies on invasive and time-demanding methods, whereas deep learning shows potential for non-invasive, accurate diagnosis despite remaining challenges.
Do deep-learning techniques provide accurate diagnosis and severity assessment for coeliac disease?
Systematic Review
Do deep-learning techniques provide accurate diagnosis and severity assessment for coeliac disease?
Deep learning techniques show high accuracy (up to 100% sensitivity/specificity) for coeliac disease diagnosis, though larger and more diverse datasets are needed.
Supports endoscopic diagnosis of coeliac disease; leaves open need for larger, prospective validation before clinical adoption.
BACKGROUND: Coeliac disease affects approximately 1% of the global population with the diagnosis often relying on invasive and time-demanding methods. Deep learning, a powerful tool in medical science, shows potential for non-invasive, accurate coeliac disease diagnosis, though challenges remain. OBJECTIVE: This systematic review aimed to evaluate the current state of deep-learning applications in coeliac disease diagnosis and identify potential areas for future research that could enhance diagnostic accuracy, sensitivity, and specificity. METHODS: A systematic review was conducted using the following databases: PubMed, Embase, Web of Science, and Scopus. PRISMA guidelines were applied. Two independent reviewers identified research articles using deep learning for coeliac disease diagnosis and severity assessment. Only original research articles with performance metrics data were included. The quality of the diagnostic accuracy studies was assessed using the QUADAS-2 tool, categorizing studies based on risk of bias and concerns about applicability. Due to heterogeneity, a narrative synthesis was conducted to describe the applications and efficacy of the deep-learning techniques (DLT) in coeliac disease diagnosis. RESULTS: The initial search across four databases yielded 417 studies with 195 being removed due to duplicity. Finally, eight studies were found to be suitable for inclusion after rigorous evaluation. They were all published between 2017 and 2023 and focused on using DLT for coeliac disease diagnosis or assessing disease severity. Different deep-learning architectures were applied. Accuracy levels ranged from 84% to 95.94% with the GoogLeNet model achieving 100% sensitivity and specificity for video capsule endoscopy images. CONCLUSIONS: DLT hold substantial potential in coeliac disease diagnosis. They offer improved accuracy and the prospect of mitigating clinician bias. However, key challenges persist, notably the requirement for more extensive and diverse datasets, especially to detect milder forms of coeliac disease. These methods are in their nascent stages, underscoring the need of integrating multiple data sources to achieve comprehensive coeliac disease diagnosis.
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Sharif et al. (2023) conducted a systematic review in Coeliac disease. Deep-learning techniques vs. Standard diagnostic methods or human experts was evaluated on Diagnostic accuracy, sensitivity, and specificity. Deep-learning models achieved high diagnostic accuracy for coeliac disease using endoscopic images, with accuracy levels ranging from 84% to 95.94% and up to 100% sensitivity and specificity in one study.
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