| Texto completo | |
| Autor(es): |
Santos-Junior, Airton Oliveira
;
Fontenele, Rocharles Cavalcante
;
Neves, Frederico Sampaio
;
Ali, Saleem
;
Jacobs, Reinhilde
;
Tanomaru-Filho, Mario
Número total de Autores: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | SCIENTIFIC REPORTS; v. 15, n. 1, p. 11-pg., 2025-02-14. |
| Resumo | |
To develop and validate an artificial intelligence (AI)-driven tool for the automatic segmentation of pulp cavity structures in maxillary premolars teeth on cone-beam computed tomography (CBCT). One hundred and eleven CBCT scans were divided into training (n = 55), validation (n = 14), and testing (n = 42) sets, with manual segmentation serving as the ground truth. The AI tool automatically segmented the testing dataset, with errors corrected by an operator to create refined 3D (R-AI) models. The overall AI performance was assessed by comparing AI and R-AI models, and thirty percent of the test sample was manually segmented to compare AI and human performance. Time-efficiency of each method was recorded in seconds (s). Statistical analysis included independent and paired t-tests to evaluate the effect of tooth type on accuracy metrics and AI versus manual segmentation. One-way ANOVA with Tukey's post hoc test was used for time efficiency analysis. A 5% significance level was used for all analyses.The AI tool demonstrated excellent performance with Dice similarity coefficients (DSC) ranging from 88% +/- 7 to 93% +/- 3 and 95% Hausdorff distances (HD) from 0.13 +/- 0.06 to 0.16 +/- 0.06 mm. Automated segmentation of maxillary second premolars performed slightly better than that of maxillary first premolars in terms of intersection over union (p = 0.005), DSC (p = 0.008), recall (p = 0.008), precision (p = 0.02), and 95% HD (p = 0.04). The AI-based approach showed higher recall (p = 0.04), accuracy (p = 0.01), and lower 95% HD than manual segmentation (p < 0.001). AI segmentation (42.8 +/- 8.4 s) was 75 times faster than manual segmentation (3218.7 +/- 692.2 s) (p < 0.001). The AI tool proved highly accurate and time-efficient, surpassing human expert performance. (AU) | |
| Processo FAPESP: | 21/11496-3 - Síntese de novo biomaterial reparador, avaliação de materiais de silicato de cálcio e análise do efeito da aplicação de biovidro no tratamento da dentina e revestimento da Guta percha na interação com cimentos obturadores biocerâmicos |
| Beneficiário: | Mario Tanomaru Filho |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 20/11012-3 - Avaliação comparativa entre micro-CT e TCFC empregando o software e-Vol DX na análise de obturação em canais ovais e espessura dentinária em canais MV1 e MV2 de molares superiores após preparo e retratamento |
| Beneficiário: | Airton Oliveira Santos Junior |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |
| Processo FAPESP: | 22/13774-3 - Algoritmo orientado por inteligência artificial para segmentação automática de estruturas endodônticas em imagens de tomografia computadorizada de feixe cônico |
| Beneficiário: | Airton Oliveira Santos Junior |
| Modalidade de apoio: | Bolsas no Exterior - Estágio de Pesquisa - Doutorado |