Ruben Pauwels1, Danieli Moura Brasil2, Mayra Cristina Yamasaki2, Reinhilde Jacobs3, Hilde Bosmans4, Deborah Queiroz Freitas2, Francisco Haiter-Neto2. 1. Aarhus Institute of Advanced Studies, Aarhus University, Aarhus, Denmark; Department of Imaging & Pathology, Biomedical Sciences Group, Catholic University of Leuven, Leuven, Belgium. Electronic address: pauwelsruben@hotmail.com. 2. Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil. 3. Department of Imaging & Pathology, Biomedical Sciences Group, Catholic University of Leuven, Leuven, Belgium; Department of Dental Medicine, Karolinska Institutet, Huddinge, Sweden. 4. Department of Imaging & Pathology, Biomedical Sciences Group, Catholic University of Leuven, Leuven, Belgium.
Abstract
OBJECTIVE: The aim of this study was to compare the diagnostic performance of convolutional neural networks (CNNs) with the performance of human observers for the detection of simulated periapical lesions on periapical radiographs. STUDY DESIGN: Ten sockets were prepared in bovine ribs. Periapical defects of 3 sizes were sequentially created. Periapical radiographs were acquired of each socket with no lesion and with each lesion size with a photostimulable storage phosphor system. Radiographs were evaluated with no filter and with 6 image filter settings. A CNN architecture was set up using Keras-TensorFlow. Separate CNNs were evaluated for randomly sampled training/validation data and for data split up by socket (5-fold cross-validation) and filter (7-fold cross-validation). CNN performance on validation data was compared with that of 3 oral radiologists for sensitivity, specificity, and area under the receiver operating characteristic curve (ROC-AUC). RESULTS: Using random sampling, the CNN showed perfect accuracy for the validation data. When data were split up by socket, the mean sensitivity, specificity, and ROC-AUC values were 0.79, 0.88, and 0.86, respectively; when split up by filter, they were 0.87, 0.98, and 0.93, respectively. For radiologists, the values were 0.58, 0.83, and 0.75, respectively. CONCLUSIONS: CNNs show promise in periapical lesion detection. The pretrained CNN model yielded in this study can be used for further training on larger samples and/or clinical radiographs.
OBJECTIVE: The aim of this study was to compare the diagnostic performance of convolutional neural networks (CNNs) with the performance of human observers for the detection of simulated periapical lesions on periapical radiographs. STUDY DESIGN: Ten sockets were prepared in bovine ribs. Periapical defects of 3 sizes were sequentially created. Periapical radiographs were acquired of each socket with no lesion and with each lesion size with a photostimulable storage phosphor system. Radiographs were evaluated with no filter and with 6 image filter settings. A CNN architecture was set up using Keras-TensorFlow. Separate CNNs were evaluated for randomly sampled training/validation data and for data split up by socket (5-fold cross-validation) and filter (7-fold cross-validation). CNN performance on validation data was compared with that of 3 oral radiologists for sensitivity, specificity, and area under the receiver operating characteristic curve (ROC-AUC). RESULTS: Using random sampling, the CNN showed perfect accuracy for the validation data. When data were split up by socket, the mean sensitivity, specificity, and ROC-AUC values were 0.79, 0.88, and 0.86, respectively; when split up by filter, they were 0.87, 0.98, and 0.93, respectively. For radiologists, the values were 0.58, 0.83, and 0.75, respectively. CONCLUSIONS: CNNs show promise in periapical lesion detection. The pretrained CNN model yielded in this study can be used for further training on larger samples and/or clinical radiographs.
Authors: P Ravi Kumar; Kolla Venkata Ravindranath; V Srilatha; Mohammed A Alobaoid; Manisha Mangesh Kulkarni; Tony Mathew; Heena Dixit Tiwari Journal: J Pharm Bioallied Sci Date: 2022-07-13
Authors: Andrej Thurzo; Wanda Urbanová; Bohuslav Novák; Ladislav Czako; Tomáš Siebert; Peter Stano; Simona Mareková; Georgia Fountoulaki; Helena Kosnáčová; Ivan Varga Journal: Healthcare (Basel) Date: 2022-07-08