1.
Zhonghua Yi Xue Za Zhi
; 102(32): 2538-2540, 2022 Aug 30.
Artículo
en Zh
| MEDLINE
| ID: mdl-36008325
RESUMEN
This study aimed to build a home use deep learning segmentation model to identify the scope of caries lesions. A total of 494 caries photographs of molars and premolars collected via endoscopy were selected. Subsequently, these photographs were labeled by physicians and underwent segmentation training by using DeepLabv3+, and then verification and evaluation were performed. The mean accuracy was 0.993, the sensitivity was 0.661, the specificity was 0.997, the Dice coefficient was 0.685, and the intersection over union (IoU) was 0.529. Therefore, the present deep learning segmentation model can identify and segment the scope of caries.
Asunto(s)
Aprendizaje Profundo , Diente Premolar , Susceptibilidad a Caries Dentarias , Diente Molar/patología
2.
Biopolymers
; 25 Suppl: S169-74, 1986.
Artículo
en Inglés
| MEDLINE
| ID: mdl-3779024