Artificial intelligence to support early diagnosis of temporomandibular disorders: A preliminary case study.
artificial intelligence
cognitive computing
decision support system
early diagnosis
temporomandibular disorders
Journal
Journal of oral rehabilitation
ISSN: 1365-2842
Titre abrégé: J Oral Rehabil
Pays: England
ID NLM: 0433604
Informations de publication
Date de publication:
Jan 2023
Jan 2023
Historique:
revised:
17
09
2022
received:
11
03
2022
accepted:
17
10
2022
pubmed:
27
10
2022
medline:
20
12
2022
entrez:
26
10
2022
Statut:
ppublish
Résumé
Temporomandibular disorders (TMDs) are disabling conditions with a negative impact on the quality of life. Their diagnosis is a complex and multi-factorial process that should be conducted by experienced professionals, and most TMDs remain often undetected. Increasing the awareness of un-experienced dentists and supporting the early TMD recognition may help reduce this gap. Artificial intelligence (AI) allowing both to process natural language and to manage large knowledge bases could support the diagnostic process. In this work, we present the experience of an AI-based system for supporting non-expert dentists in early TMD recognition. The system was based on commercially available AI services. The prototype development involved a preliminary domain analysis and relevant literature identification, the implementation of the core cognitive computing services, the web interface and preliminary testing. Performance evaluation included a retrospective review of seven available clinical cases, together with the involvement of expert professionals for usability testing. The system comprises one module providing possible diagnoses according to a list of symptoms, and a second one represented by a question and answer tool, based on natural language. We found that, even when using commercial services, the training guided by experts is a key factor and that, despite the generally positive feedback, the application's best target is untrained professionals. We provided a preliminary proof of concept of the feasibility of implementing an AI-based system aimed to support non-specialists in the early identification of TMDs, possibly allowing a faster and more frequent referral to second-level medical centres. Our results showed that AI is a useful tool to improve TMD detection by facilitating a primary diagnosis.
Sections du résumé
BACKGROUND
BACKGROUND
Temporomandibular disorders (TMDs) are disabling conditions with a negative impact on the quality of life. Their diagnosis is a complex and multi-factorial process that should be conducted by experienced professionals, and most TMDs remain often undetected. Increasing the awareness of un-experienced dentists and supporting the early TMD recognition may help reduce this gap. Artificial intelligence (AI) allowing both to process natural language and to manage large knowledge bases could support the diagnostic process.
OBJECTIVE
OBJECTIVE
In this work, we present the experience of an AI-based system for supporting non-expert dentists in early TMD recognition.
METHODS
METHODS
The system was based on commercially available AI services. The prototype development involved a preliminary domain analysis and relevant literature identification, the implementation of the core cognitive computing services, the web interface and preliminary testing. Performance evaluation included a retrospective review of seven available clinical cases, together with the involvement of expert professionals for usability testing.
RESULTS
RESULTS
The system comprises one module providing possible diagnoses according to a list of symptoms, and a second one represented by a question and answer tool, based on natural language. We found that, even when using commercial services, the training guided by experts is a key factor and that, despite the generally positive feedback, the application's best target is untrained professionals.
CONCLUSION
CONCLUSIONS
We provided a preliminary proof of concept of the feasibility of implementing an AI-based system aimed to support non-specialists in the early identification of TMDs, possibly allowing a faster and more frequent referral to second-level medical centres. Our results showed that AI is a useful tool to improve TMD detection by facilitating a primary diagnosis.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
31-38Informations de copyright
© 2022 John Wiley & Sons Ltd.
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