Application of deep learning and feature selection technique on external root resorption identification on CBCT images.

BACKGROUND: Artificial intelligence has been proven to improve the identification of various maxillofacial lesions. The aim of the current study is two-fold: to assess the performance of four deep learning models (DLM) in external root resorption (ERR) identification and to assess the effect of combining feature selection technique (FST) with DLM on their ability in ERR identification. METHODS: External root resorption was simulated on 88 extracted premolar teeth using tungsten bur in different depths (0.5 mm, 1 mm, and 2 mm). All teeth were scanned using a Cone beam CT (Carestream Dental, Atlanta, GA). Afterward, a training (70%), validation (10%), and test (20%) dataset were established. The performance of four DLMs including Random Forest (RF) + Visual Geometry Group 16 (VGG), RF + EfficienNetB4 (EFNET), Support Vector Machine (SVM) + VGG, and SVM + EFNET) and four hybrid models (DLM + FST: (i) FS + RF + VGG, (ii) FS + RF + EFNET, (iii) FS + SVM + VGG and (iv) FS + SVM + EFNET) was compared. Five performance parameters were assessed: classification accuracy, F1-score, precision, specificity, and error rate. FST algorithms (Boruta and Recursive Feature Selection) were combined with the DLMs to assess their performance. RESULTS: RF + VGG exhibited the highest performance in identifying ERR, followed by the other tested models. Similarly, FST combined with RF + VGG outperformed other models with classification accuracy, F1-score, precision, and specificity of 81.9%, weighted accuracy of 83%, and area under the curve (AUC) of 96%. Kruskal Wallis test revealed a significant difference (p = 0.008) in the prediction accuracy among the eight DLMs. CONCLUSION: In general, all DLMs have similar performance on ERR identification. However, the performance can be improved by combining FST with DLMs.

Anthropometric Study of Three-Dimensional Facial Morphology in Malay Adults.

OBJECTIVES: To establish the three-dimensional (3D) facial soft tissue morphology of adult Malaysian subjects of the Malay ethnic group; and to determine the morphological differences between the genders, using a non-invasive stereo-photogrammetry 3D camera. MATERIAL AND METHODS: One hundred and nine subjects participated in this research, 54 Malay men and 55 Malay women, aged 20-30 years old with healthy BMI and with no adverse skeletal deviation. Twenty-three facial landmarks were identified on 3D facial images captured using a VECTRA M5-360 Head System (Canfield Scientific Inc, USA). Two angular, 3 ratio and 17 linear measurements were identified using Canfield Mirror imaging software. Intra- and inter-examiner reliability tests were carried out using 10 randomly selected images, analyzed using the intra-class correlation coefficient (ICC). Multivariate analysis of variance (MANOVA) was carried out to investigate morphologic differences between genders. RESULTS: ICC scores were generally good for both intra-examiner (range 0.827-0.987) and inter-examiner reliability (range 0.700-0.983) tests. Generally, all facial measurements were larger in men than women, except the facial profile angle which was larger in women. Clinically significant gender dimorphisms existed in biocular width, nose height, nasal bridge length, face height and lower face height values (mean difference > 3mm). Clinical significance was set at 3mm. CONCLUSION: Facial soft tissue morphological values can be gathered efficiently and measured effectively from images captured by a non-invasive stereo-photogrammetry 3D camera. Adult men in Malaysia when compared to women had a wider distance between the eyes, a longer and more prominent nose and a longer face.