Performance of a commercially available Generative Pre-trained Transformer (GPT) in describing radiolucent lesions in panoramic radiographs and establishing differential diagnoses.

Autor: Silva TP; Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Piracicaba, Sao Paulo, 13414-903, Brazil., Andrade-Bortoletto MFS; Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Piracicaba, Sao Paulo, 13414-903, Brazil., Ocampo TSC; Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Piracicaba, Sao Paulo, 13414-903, Brazil., Alencar-Palha C; Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Piracicaba, Sao Paulo, 13414-903, Brazil., Bornstein MM; Department of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, Basel, 4058, Switzerland., Oliveira-Santos C; Department of Diagnosis and Oral Health, University of Louisville School of Dentistry, Louisville, KY, 40202, USA., Oliveira ML; Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Piracicaba, Sao Paulo, 13414-903, Brazil. matheus.limadeoliveira@unibas.ch.; Department of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, Basel, 4058, Switzerland. matheus.limadeoliveira@unibas.ch.
Jazyk: angličtina
Zdroj: Clinical oral investigations [Clin Oral Investig] 2024 Mar 09; Vol. 28 (3), pp. 204. Date of Electronic Publication: 2024 Mar 09.
DOI: 10.1007/s00784-024-05587-5
Abstrakt: Objectives: To evaluate the performance of a commercially available Generative Pre-trained Transformer (GPT) in describing and establishing differential diagnoses for radiolucent lesions in panoramic radiographs.
Materials and Methods: Twenty-eight panoramic radiographs, each containing a single radiolucent lesion, were evaluated in consensus by three examiners and a commercially available ChatGPT-3.5 model. They provided descriptions regarding internal structure (radiodensity, loculation), periphery (margin type, cortication), shape, location (bone, side, region, teeth/structures), and effects on adjacent structures (effect, adjacent structure). Diagnostic impressions related to origin, behavior, and nature were also provided. The GPT program was additionally prompted to provide differential diagnoses. Keywords used by the GPT program were compared to those used by the examiners and scored as 0 (incorrect), 0.5 (partially correct), or 1 (correct). Mean score values and standard deviation were calculated for each description. Performance in establishing differential diagnoses was assessed using Rank-1, -2, and - 3.
Results: Descriptions of margination, affected bone, and origin received the highest scores: 0.93, 0.93, and 0.87, respectively. Shape, region, teeth/structures, effect, affected region, and nature received considerably lower scores ranging from 0.22 to 0.50. Rank-1, -2, and - 3 demonstrated accuracy in 25%, 57.14%, and 67.85% of cases, respectively.
Conclusion: The performance of the GPT program in describing and providing differential diagnoses for radiolucent lesions in panoramic radiographs is variable and at this stage limited in its use for clinical application.
Clinical Relevance: Understanding the potential role of GPT systems as an auxiliary tool in image interpretation is imperative to validate their clinical applicability.
(© 2024. The Author(s).)
Databáze: MEDLINE
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