Evaluation of different color measurement methods for determining tooth color.

AIM: The purpose of this study is to develop software at a low cost that enables the detection of tooth colors by capturing photographs using various devices, and to compare its effectiveness with existing expensive methods. MATERIAL AND METHODS: A total of 60 anterior central incisor teeth from 30 individuals were included in the study. The CIELAB values (L,a,b) of each tooth were measured using a spectrophotometer, which is considered the gold standard. Subsequently, photographs of the teeth were taken using four different smartphones (iPhone- Xiaomi) and one digital camera (Canon). These images were then subjected to image processing techniques and compared with measurements obtained through computer-based analysis in order to assess the correlation. Data with three or more groups, the Kruskal-Wallis H test was utilized, and multiple comparisons were conducted using the Dunn test. A significance level of p<0.05 was considered. RESULTS: Upon examining the results of multiple comparisons, a statistically significant difference was observed (p<0.001) between the DeltaE values obtained from the camera of the iPhone and those obtained from the Canon DSLR and Xiaomi cameras. The iPhone cameras yielded result values ranging from 2.68 to 2.90 for DeltaE. CONCLUSIONS: It is reported that color determination methods based on image processing of photographs taken with iPhone mobile phones could potentially gain an advantageous position in routine clinical practice, as compared to spectrophotometry.

A fine-tuned YOLOv5 deep learning approach for real-time house number detection.

Detection of small objects in natural scene images is a complicated problem due to the blur and depth found in the images. Detecting house numbers from the natural scene images in real-time is a computer vision problem. On the other hand, convolutional neural network (CNN) based deep learning methods have been widely used in object detection in recent years. In this study, firstly, a classical CNN-based approach is used to detect house numbers with locations from natural images in real-time. Faster R-CNN, MobileNet, YOLOv4, YOLOv5 and YOLOv7, among the commonly used CNN models, models were applied. However, satisfactory results could not be obtained due to the small size and variable depth of the door plate objects. A new approach using the fine-tuning technique is proposed to improve the performance of CNN-based deep learning models. Experimental evaluations were made on real data from Kayseri province. Classic Faster R-CNN, MobileNet, YOLOv4, YOLOv5 and YOLOv7 methods yield f1 scores of 0.763, 0.677, 0.880, 0.943 and 0.842, respectively. The proposed fine-tuned Faster R-CNN, MobileNet, YOLOv4, YOLOv5, and YOLOv7 approaches achieved f1 scores of 0.845, 0.775, 0.932, 0.972 and 0.889, respectively. Thanks to the proposed fine-tuned approach, the f1 score of all models has increased. Regarding the run time of the methods, classic Faster R-CNN detects 0.603 seconds, while fine-tuned Faster R-CNN detects 0.633 seconds. Classic MobileNet detects 0.046 seconds, while fine-tuned MobileNet detects 0.048 seconds. Classic YOLOv4 and fine-tuned YOLOv4 detect 0.235 and 0.240 seconds, respectively. Classic YOLOv5 and fine-tuned YOLOv5 detect 0.015 seconds, and classic YOLOv7 and fine-tuned YOLOv7 detect objects in 0.009 seconds. While the YOLOv7 model was the fastest running model with an average running time of 0.009 seconds, the proposed fine-tuned YOLOv5 approach achieved the highest performance with an f1 score of 0.972.

Developing deep learning methods for classification of teeth in dental panoramic radiography.

OBJECTIVES: We aimed to develop an artificial intelligence-based clinical dental decision-support system using deep-learning methods to reduce diagnostic interpretation error and time and increase the effectiveness of dental treatment and classification. STUDY DESIGN: We compared the performance of 2 deep-learning methods, Faster Regions With the Convolutional Neural Networks (R-CNN) and You Only Look Once V4 (YOLO-V4), for tooth classification in dental panoramic radiography to determine which is more successful in terms of accuracy, time, and detection ability. Using a method based on deep-learning models trained on a semantic segmentation task, we analyzed 1200 panoramic radiographs selected retrospectively. In the classification process, our model identified 36 classes, including 32 teeth and 4 impacted teeth. RESULTS: The YOLO-V4 method achieved a mean 99.90% precision, 99.18% recall, and 99.54% F1 score. The Faster R-CNN method achieved a mean 93.67% precision, 90.79% recall, and 92.21% F1 score. Experimental evaluations showed that the YOLO-V4 method outperformed the Faster R-CNN method in terms of accuracy of predicted teeth in the tooth classification process, speed of tooth classification, and ability to detect impacted and erupted third molars. CONCLUSIONS: The YOLO-V4 method outperforms the Faster R-CNN method in terms of accuracy of tooth prediction, speed of detection, and ability to detect impacted third molars and erupted third molars. The proposed deep learning based methods can assist dentists in clinical decision making, save time, and reduce the negative effects of stress and fatigue in daily practice.