Utilizing deep learning for automated detection of oral lesions: A multicenter study.
Oral Oncol
; 155: 106873, 2024 Aug.
Article
en En
| MEDLINE
| ID: mdl-38833826
ABSTRACT
OBJECTIVES:
We aim to develop a YOLOX-based convolutional neural network model for the precise detection of multiple oral lesions, including OLP, OLK, and OSCC, in patient photos. MATERIALS ANDMETHODS:
We collected 1419 photos for model development and evaluation, conducting both a comparative analysis to gauge the model's capabilities and a multicenter evaluation to assess its diagnostic aid, where 24 participants from 14 centers across the nation were invited. We further integrated this model into a mobile application for rapid and accurate diagnostics.RESULTS:
In the comparative analysis, our model overperformed the senior group (comprising three most experienced experts with more than 10 years of experience) in macro-average recall (85 % vs 77.5 %), precision (87.02 % vs 80.29 %), and specificity (95 % vs 92.5 %). In the multicenter model-assisted diagnosis evaluation, the dental, general, and community hospital groups showed significant improvement when aided by the model, reaching a level comparable to the senior group, with all macro-average metrics closely aligning or even surpassing with those of the latter (recall of 78.67 %, 74.72 %, 83.54 % vs 77.5 %, precision of 80.56 %, 76.42 %, 85.15 % vs 80.29 %, specificity of 92.89 %, 91.57 %, 94.51 % vs 92.5 %).CONCLUSION:
Our model exhibited a high proficiency in detection of oral lesions, surpassing the performance of highly experienced specialists. The model can also help specialists and general dentists from dental and community hospitals in diagnosing oral lesions, reaching the level of highly experienced specialists. Moreover, our model's integration into a mobile application facilitated swift and precise diagnostic procedures.Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Neoplasias de la Boca
/
Aprendizaje Profundo
Límite:
Humans
Idioma:
En
Revista:
Oral Oncol
Asunto de la revista:
NEOPLASIAS
Año:
2024
Tipo del documento:
Article
País de afiliación:
China