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Article

Artificial Intelligence for Classifying the Relationship between Impacted Third Molar and Mandibular Canal on Panoramic Radiographs

by
Antonio Lo Casto
1,
Giacomo Spartivento
1,*,
Viviana Benfante
2,3,4,
Riccardo Di Raimondo
5,6,
Muhammad Ali
2,3,
Domenico Di Raimondo
3,
Antonino Tuttolomondo
3,
Alessandro Stefano
4,
Anthony Yezzi
7 and
Albert Comelli
2
1
Section of Radiological Sciences, Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, 90127 Palermo, Italy
2
Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy
3
Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy
4
Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy
5
Postgraduate Section of Periodontology, Faculty of Odontology, University Complutense, 28040 Madrid, Spain
6
Postgraduate Section of Oral Surgery, Periodontology and Implant, University Sur Mississippi, Spain Istitutions, 28040 Madrid, Spain
7
Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
*
Author to whom correspondence should be addressed.
Life 2023, 13(7), 1441; https://doi.org/10.3390/life13071441
Submission received: 23 May 2023 / Revised: 9 June 2023 / Accepted: 21 June 2023 / Published: 26 June 2023
(This article belongs to the Section Radiobiology and Nuclear Medicine)
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Abstract

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.
Keywords: contact relationship; convolutional neural network; inferior alveolar nerve; mandibular third molar; panoramic radiograph; ResNet-152; VGG-19 contact relationship; convolutional neural network; inferior alveolar nerve; mandibular third molar; panoramic radiograph; ResNet-152; VGG-19

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MDPI and ACS Style

Lo Casto, A.; Spartivento, G.; Benfante, V.; Di Raimondo, R.; Ali, M.; Di Raimondo, D.; Tuttolomondo, A.; Stefano, A.; Yezzi, A.; Comelli, A. 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

AMA Style

Lo Casto A, Spartivento G, Benfante V, Di Raimondo R, Ali M, Di Raimondo D, Tuttolomondo A, Stefano A, Yezzi A, Comelli A. Artificial Intelligence for Classifying the Relationship between Impacted Third Molar and Mandibular Canal on Panoramic Radiographs. Life. 2023; 13(7):1441. https://doi.org/10.3390/life13071441

Chicago/Turabian Style

Lo Casto, Antonio, Giacomo Spartivento, Viviana Benfante, Riccardo Di Raimondo, Muhammad Ali, Domenico Di Raimondo, Antonino Tuttolomondo, Alessandro Stefano, Anthony Yezzi, and Albert Comelli. 2023. "Artificial Intelligence for Classifying the Relationship between Impacted Third Molar and Mandibular Canal on Panoramic Radiographs" Life 13, no. 7: 1441. https://doi.org/10.3390/life13071441

APA Style

Lo Casto, A., Spartivento, G., Benfante, V., Di Raimondo, R., Ali, M., Di Raimondo, D., Tuttolomondo, A., Stefano, A., Yezzi, A., & Comelli, A. (2023). Artificial Intelligence for Classifying the Relationship between Impacted Third Molar and Mandibular Canal on Panoramic Radiographs. Life, 13(7), 1441. https://doi.org/10.3390/life13071441

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