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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Francesca De Angelis & Nicola Pranno & Alessio Franchina & Stefano Di Carlo & Edoardo Brauner & Agnese Ferri & Gerardo Pellegrino & Emma Grecchi & Funda Goker & Luigi Vito Stefanelli, 2022. "Artificial Intelligence: A New Diagnostic Software in Dentistry: A Preliminary Performance Diagnostic Study," IJERPH, MDPI, vol. 19(3), pages 1-10, February.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1728-:d:741084
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    References listed on IDEAS

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    1. Tim Joda & Michael M. Bornstein & Ronald E. Jung & Marco Ferrari & Tuomas Waltimo & Nicola U. Zitzmann, 2020. "Recent Trends and Future Direction of Dental Research in the Digital Era," IJERPH, MDPI, vol. 17(6), pages 1-8, March.
    2. Kuofeng Hung & Andy Wai Kan Yeung & Ray Tanaka & Michael M. Bornstein, 2020. "Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice," IJERPH, MDPI, vol. 17(12), pages 1-18, June.
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    Cited by:

    1. Mengting Cheng & Xianmiao Li & Jicheng Xu, 2022. "Promoting Healthcare Workers’ Adoption Intention of Artificial-Intelligence-Assisted Diagnosis and Treatment: The Chain Mediation of Social Influence and Human–Computer Trust," IJERPH, MDPI, vol. 19(20), pages 1-19, October.

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