Anselmo Garcia Cantu1, Sascha Gehrung1, Joachim Krois1, Akhilanand Chaurasia2, Jesus Gomez Rossi1, Robert Gaudin3, Karim Elhennawy4, Falk Schwendicke5. 1. Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Germany. 2. Department of Oral Medicine and Radiology, King George's Medical University, Lucknow, India. 3. Department of Oral- and Maxillofacial Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany. 4. Department of Orthodontics, Dentofacial Orthopedics and Pedodontics, Charité - Universitätsmedizin Berlin, Germany. 5. Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Germany. Electronic address: falk.schwendicke@charite.de.
Abstract
OBJECTIVES: We aimed to apply deep learning to detect caries lesions of different radiographic extension on bitewings, hypothesizing it to be significantly more accurate than individual dentists. METHODS: 3686 bitewing radiographs were assessed by four experienced dentists. Caries lesions were marked in a pixelwise fashion. The union of all pixels was defined as reference test. The data was divided into a training (3293), validation (252) and test dataset (141). We applied a convolutional neural network (U-Net) and used the Intersection-over-Union as validation metric. The performance of the trained neural network on the test dataset was compared against that of seven independent using tooth-level accuracy metrics. Stratification according to lesion depth (enamel lesions E1/2, dentin lesions into middle or inner third D2/3) was applied. RESULTS: The neural network showed an accuracy of 0.80; dentists' mean accuracy was significantly lower at 0.71 (min-max: 0.61-0.78, p < 0.05). The neural network was significantly more sensitive than dentists (0.75 versus 0.36 (0.19-0.65; p = 0.006), while its specificity was not significantly lower (0.83) than those of the dentists (0.91 (0.69-0.98; p > 0.05); p > 0.05). The neural network showed robust sensitivities at or above 0.70 for both initial and advanced lesions. Dentists largely showed low sensitivities for initial lesions (all except one dentist showed sensitivities below 0.25), while those for advanced ones were between 0.40 and 0.75. CONCLUSIONS: To detect caries lesions on bitewing radiographs, a deep neural network was significantly more accurate than dentists. CLINICAL SIGNIFICANCE: Deep learning may assist dentists to detect especially initial caries lesions on bitewings. The impact of using such models on decision-making should be explored.
OBJECTIVES: We aimed to apply deep learning to detect caries lesions of different radiographic extension on bitewings, hypothesizing it to be significantly more accurate than individual dentists. METHODS: 3686 bitewing radiographs were assessed by four experienced dentists. Caries lesions were marked in a pixelwise fashion. The union of all pixels was defined as reference test. The data was divided into a training (3293), validation (252) and test dataset (141). We applied a convolutional neural network (U-Net) and used the Intersection-over-Union as validation metric. The performance of the trained neural network on the test dataset was compared against that of seven independent using tooth-level accuracy metrics. Stratification according to lesion depth (enamel lesions E1/2, dentin lesions into middle or inner third D2/3) was applied. RESULTS: The neural network showed an accuracy of 0.80; dentists' mean accuracy was significantly lower at 0.71 (min-max: 0.61-0.78, p < 0.05). The neural network was significantly more sensitive than dentists (0.75 versus 0.36 (0.19-0.65; p = 0.006), while its specificity was not significantly lower (0.83) than those of the dentists (0.91 (0.69-0.98; p > 0.05); p > 0.05). The neural network showed robust sensitivities at or above 0.70 for both initial and advanced lesions. Dentists largely showed low sensitivities for initial lesions (all except one dentist showed sensitivities below 0.25), while those for advanced ones were between 0.40 and 0.75. CONCLUSIONS: To detect caries lesions on bitewing radiographs, a deep neural network was significantly more accurate than dentists. CLINICAL SIGNIFICANCE: Deep learning may assist dentists to detect especially initial caries lesions on bitewings. The impact of using such models on decision-making should be explored.
Authors: Naseer Ahmed; Maria Shakoor Abbasi; Filza Zuberi; Warisha Qamar; Mohamad Syahrizal Bin Halim; Afsheen Maqsood; Mohammad Khursheed Alam Journal: Biomed Res Int Date: 2021-06-22 Impact factor: 3.411
Authors: Tanya Walsh; Richard Macey; Philip Riley; Anne-Marie Glenny; Falk Schwendicke; Helen V Worthington; Janet E Clarkson; David Ricketts; Ting-Li Su; Anita Sengupta Journal: Cochrane Database Syst Rev Date: 2021-03-15