title: [Accuracy of computer aided measurement for detecting dental proximal caries lesions in images of cone-beam computed tomography].
abstract: Objective:To establish and validate a computer program used to aid the detection of dental proximal caries in the images cone beam computed tomography (CBCT) images.Methods:According to the characteristics of caries lesions in X-ray images, a computer aided detection program for proximal caries was established with Matlab and Visual C++. The whole process for caries lesion detection included image import and preprocessing, measuring average gray value of air area, choosing region of interest and calculating gray value, defining the caries areas. The program was used to examine 90 proximal surfaces from 45 extracted human teeth collected from Peking University School and Hospital of Stomatology. The teeth were then scanned with a CBCT scanner (Promax 3D). The proximal surfaces of the teeth were respectively detected by caries detection program and scored by human observer for the extent of lesions with 6-level-scale. With histologic examination serving as the reference standard, the caries detection program and the human observer performances were assessed with receiver operating characteristic (ROC) curves. Studentt-test was used to analyze the areas under the ROC curves (AUC) for the differences between caries detection program and human observer. Spearman correlation coefficient was used to analyze the detection accuracy of caries depth.Results:For the diagnosis of proximal caries in CBCT images, the AUC values of human observers and caries detection program were 0.632 and 0.703, respectively. There was a statistically significant difference between the AUC values (P=0.023). The correlation between program performance and gold standard (correlation coefficientr(s)=0.525) was higher than that of observer performance and gold standard (r(s)=0.457) and there was a statistically significant difference between the correlation coefficients (P=0.000).Conclusions:The program that automatically detects dental proximal caries lesions could improve the diagnostic value of CBCT images.
authors.affiliation: Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China (Present address: Department of Oral and Maxillofacial Radiology, Stomalogical Hospital of Tianjin, Tianjin 300041, China).
authors.forename: Z L
authors.initials: ZL
authors.lastname: Zhang
authors.affiliation: Signal & Image Processing Laboratory, School of Electronic Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
authors.forename: J P
authors.initials: JP
authors.lastname: Li
authors.affiliation: Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China.
authors.forename: G
authors.initials: G
authors.lastname: Li
authors.affiliation: Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China.
authors.forename: X C
authors.initials: XC
authors.lastname: Ma
mesh_terms: D054893:Cone-Beam Computed Tomography; D003731:Dental Caries / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D015588:Observer Variation; D012372:ROC Curve; D019252:Radiography, Dental, Digital; D012015:Reference Standards; D012984:Software
keywords: Cone-beam computed tomography; Dental caries; Diagnosis, computer-assisted; Software