RT Journal Article
T1 Artificial Intelligence for Classifying the Relationship between Impacted Third Molar and Mandibular Canal on Panoramic Radiographs
A1 Lo Casto, Antonio
A1 Spartivento, Giacomo
A1 Benfante, Viviana
A1 Di Raimondo, Riccardo
A1 Ali, Muhammad
A1 DI Raimondo, Domenico
A1 Tuttolomondo, Antonino
A1 Stefano, Alessandro
A1 Yezzi, Anthony
A1 Comelli, Albert
A2 Edet Ekpenyong, Andrew
AB The purpose of this investigation was to evaluate the diagnostic performance of two convolutional neural networks (CNNs), namely ResNet-152 and VGG-19, in analyzing, on panoramic images, the rapport that exists between the lower third molar (MM3) and the mandibular canal (MC), and to compare this performance with that of an inexperienced observer (a sixth year dental student). Utilizing the k-fold cross-validation technique, 142 MM3 images, cropped from 83 panoramic images, were split into 80% as training and validation data and 20% as test data. They were subsequently labeled by an experienced radiologist as the gold standard. In order to compare the diagnostic capabilities of CNN algorithms and the inexperienced observer, the diagnostic accuracy, sensitivity, specificity, and positive predictive value (PPV) were determined. ResNet-152 achieved a mean sensitivity, specificity, PPV, and accuracy, of 84.09%, 94.11%, 92.11%, and 88.86%, respectively. VGG-19 achieved 71.82%, 93.33%, 92.26%, and 85.28% regarding the aforementioned characteristics. The dental student’s diagnostic performance was respectively 69.60%, 53.00%, 64.85%, and 62.53%. This work demonstrated the potential use of deep CNN architecture for the identification and evaluation of the contact between MM3 and MC in panoramic pictures. In addition, CNNs could be a useful tool to assist inexperienced observers in more accurately identifying contact relationships between MM3 and MC on panoramic images.
PB MDPI
YR 2023
FD 2023-06-26
LK https://hdl.handle.net/20.500.14352/104962
UL https://hdl.handle.net/20.500.14352/104962
LA eng
NO Lo Casto A, Spartivento G, Benfante V, Di Raimondo R, Ali M, Di Raimondo D, et al. Artificial Intelligence for Classifying the Relationship between Impacted Third Molar and Mandibular Canal on Panoramic Radiographs. Life 2023;13:1441. https://doi.org/10.3390/life13071441.
NO 2023 Descuento MDPI
DS Docta Complutense
RD 3 abr 2025