Szczegóły publikacji
Opis bibliograficzny
Artificial Intelligence (AI) assessment of pediatric dental panoramic radiographs (DPRs): a clinical study / Natalia Turosz, Kamila CHĘCIŃSKA, Maciej Chęciński, Karolina Lubecka, Filip Bliźniak, Maciej Sikora // Pediatric Reports [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2036-7503. — 2024 — vol. 16 iss. 3, s. 794–805. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 803–805, Abstr. — Publikacja dostępna online od: 2024-09-11
Autorzy (6)
- Turosz Natalia
- AGHChęcińska Kamila
- Chęciński Maciej Adam
- Lubecka Karolina
- Bliźniak Filip
- Sikora Maciej
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 155587 |
|---|---|
| Data dodania do BaDAP | 2024-10-02 |
| Tekst źródłowy | URL |
| DOI | 10.3390/pediatric16030067 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Pediatric Reports |
Abstract
This clinical study aimed to evaluate the sensitivity, specificity, accuracy, and precision of artificial intelligence (AI) in assessing permanent teeth in pediatric patients. Over one thousand consecutive DPRs taken in Kielce, Poland, with the Carestream CS9600 device were screened. In the study material, 35 dental panoramic radiographs (DPRs) of patients of developmental age were identified and included. They were automatically evaluated with an AI algorithm. The DPRs were then analyzed by researchers. The status of the following dichotomous variables was assessed: (1) decay, (2) missing tooth, (3) filled tooth, (4) root canal filling, and (5) endodontic lesion. The results showed high specificity and accuracy (all above 85%) in detecting caries, dental fillings, and missing teeth but low precision. This study provided a detailed assessment of AI performance in a previously neglected age group. In conclusion, the overall accuracy of AI algorithms for evaluating permanent dentition in dental panoramic radiographs is lower for pediatric patients than adults or the entire population. Hence, identifying primary teeth should be implemented in AI-driven software, at least so as to ignore them when assessing mixed dentition (ClinicalTrials.gov registration number: NCT06258798).
