A carregar...
Publicação
Diagnostic performance of AI-assisted software in sports dentistry : a validation study
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 452.18 KB | Adobe PDF |
Orientador(es)
Resumo(s)
Artificial Intelligence (AI) applications in sports dentistry have the potential to improve early detection and diagnosis. We aimed to validate the diagnostic performance of AI-assisted software in detecting dental caries, periodontitis, and tooth wear using panoramic radiographs in elite athletes. This cross-sectional validation study included secondary data from 114 elite athletes from the Sports Dentistry department at Egas Moniz Dental Clinic. The AI software’s performance was compared to clinically validated assessments. Dental caries and tooth wear were inspected clinically and confirmed radiographically. Periodontitis was registered through self-reports. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), as well as the area under the curve and respective 95% confidence intervals. Inter-rater agreement was assessed using Cohen’s kappa statistic. The AI software showed high reproducibility, with kappa values of 0.82 for caries, 0.91 for periodontitis, 0.96 for periapical lesions, and 0.76 for tooth wear. Sensitivity was highest for periodontitis (1.00; AUC = 0.84), moderate for caries (0.74; AUC = 0.69), and lower for tooth wear (0.53; AUC = 0.68). Full agreement between AI and clinical reference was achieved in 86.0% of cases. The software generated a median of 3 AI-specific suggestions per case (range: 0–16). In 21.9% of cases, AI’s interpretation of periodontal level was deemed inadequate; among these, only 2 cases were clinically confirmed as periodontitis. Of the 34 false positives for periodontitis, 32.4% were misidentified by the AI. The AI-assisted software demonstrated substantial agreement with clinical diagnosis, particularly for periodontitis and caries. The relatively high false-positive rate for periodontitis and limited sensitivity for tooth wear underscore the need for cautious clinical integration, supervision, and further model refinements. However, this software did show overall adequate performance for application in Sports Dentistry.
Descrição
Palavras-chave
artificial intelligence diagnostic performance sports dentistry panoramic radiography validation study
Contexto Educativo
Citação
Júdice A, Brandão D, Rodrigues C, Simões C, Nogueira G, Machado V, Ferreira LMA, Ferreira D, Proença L, Botelho J, et al. Diagnostic Performance of AI-Assisted Software in Sports Dentistry: A Validation Study. AI. 2025; 6(10):255. https://doi.org/10.3390/ai6100255
Editora
MDPI