Artificial Intelligence: A New Diagnostic Software in Dentistry: A Preliminary Performance Diagnostic Study

Francesca De Angelis, Nicola Pranno, Alessio Franchina, Stefano Di Carlo, Edoardo Brauner, Agnese Ferri, Gerardo Pellegrino, Emma Grecchi, Funda Goker and Luigi Vito Stefanelli
Additional contact information
Francesca De Angelis: Department of Oral and Maxillofacial Sciences, Sapienza University of Rome, Via Caserta 6, 00161 Rome, Italy
Nicola Pranno: Department of Oral and Maxillofacial Sciences, Sapienza University of Rome, Via Caserta 6, 00161 Rome, Italy
Alessio Franchina: Private Practice, Via Legione Gallieno 44, 36100 Vicenza, Italy
Stefano Di Carlo: Department of Oral and Maxillofacial Sciences, Sapienza University of Rome, Via Caserta 6, 00161 Rome, Italy
Edoardo Brauner: Department of Oral and Maxillofacial Sciences, Sapienza University of Rome, Via Caserta 6, 00161 Rome, Italy
Agnese Ferri: Oraland Maxillofacial Surgery Division, DIBINEM, University of Bologna, 40125 Bologna, Italy
Gerardo Pellegrino: Oraland Maxillofacial Surgery Division, DIBINEM, University of Bologna, 40125 Bologna, Italy
Emma Grecchi: Chirurgiche ed Odontoiatriatriche, Dipartimento di Scienze Biomediche, University of Milan, Via Della Commenda 9, 20122 Milano, Italy
Funda Goker: Chirurgiche ed Odontoiatriatriche, Dipartimento di Scienze Biomediche, University of Milan, Via Della Commenda 9, 20122 Milano, Italy
Luigi Vito Stefanelli: Department of Oral and Maxillofacial Sciences, Sapienza University of Rome, Via Caserta 6, 00161 Rome, Italy

IJERPH, 2022, vol. 19, issue 3, 1-10

Abstract: Background: Artificial intelligence (AI) has taken hold in public health because more and more people are looking to make a diagnosis using technology that allows them to work faster and more accurately, reducing costs and the number of medical errors. Methods: In the present study, 120 panoramic X-rays (OPGs) were randomly selected from the Department of Oral and Maxillofacial Sciences of Sapienza University of Rome, Italy. The OPGs were acquired and analyzed using Apox, which takes a panoramic X-rayand automatically returns the dental formula, the presence of dental implants, prosthetic crowns, fillings and root remnants. A descriptive analysis was performed presenting the categorical variables as absolute and relative frequencies. Results: In total, the number of true positive (TP) values was 2.195 (19.06%); true negative (TN), 8.908 (77.34%); false positive (FP), 132 (1.15%); and false negative (FN), 283 (2.46%). The overall sensitivity was 0.89, while the overall specificity was 0.98. Conclusions: The present study shows the latest achievements in dentistry, analyzing the application and credibility of a new diagnostic method to improve the work of dentists and the patients’ care.

Keywords: artificial intelligence; machine learning; digital dentistry; dental radiology; accuracy (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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