Diagnostic Performance of Ai vs Junior Dentists
Comparison of Artificial Intelligence vs. Junior Dentists Diagnostic Performance for Radiographic Caries & Periapical Lesion Detection
OBJECTIVES
- Compare diagnosis of Periapical lesions & Proximal caries by AI and junior dentists
- Evaluate Validity of a deep learning based convolutional neural network (CNN) for detecting proximal caries lesions, periapical lesions compared to junior dentists.
Studieöversikt
Status
Status
Betingelser
Betingelser
Detaljerad beskrivning
Statement of problem Current evidence supports the use of AI as a valuable diagnostic platform adjunct for detecting periapical lesions. However, well-designed, high-quality randomized clinical trials are required to assess the potential implementation of AI in the routine practice of periapical lesion diagnosis. In addition, most studies were done on OPG and CBCT not on periapical radiographs. Evidence suggests that AI-assisted systems are effective in detecting large, distinct periapical lesions. However, their performance drops in more complex cases, such as with root-filled teeth and in small periodical lesions The performance of AI in caries detection remains moderate, and its periapical status assessment is insufficient due to high false-positive rates
Research Questions
- Does Trained AI outperform junior dentists in basic diagnostic tasks, specifically in detecting tooth caries and periapical lesions through radiographic interpretation?
- Does AI increase the accuracy of lesion detection?
- Is AI more accurate than junior dentists in detecting carious lesions or periapical lesions?
- Is there a difference between diagnosis of Caries vs Periapical lesions through AI tools compared to junior dentists?
Studietyp
Studietyp
Inskrivning (Beräknad)
Inskrivning
Kontakter och platser
Studiekontakt
Studiekontakt
- Namn: Susan Hattar, PhD
- Telefonnummer: +962795642613
- E-post: s.hattar@ju.edu.jo
Studieorter
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Amman, Jordanien, 11181
- The university of Jordan
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Deltagandekriterier
Urvalskriterier
Urvalskriterier
Åldrar som är berättigade till studier
Tar emot friska volontärer
Kön som är behöriga för studier
Testmetod
Studera befolkning
Patients over 18 years of age that were clear and displayed the full anatomical crown and root apex.
Beskrivning
Inclusion Criteria:
- Bitewings obtained from patients treated at Jordan University Hospital. Only bitewings of permanent maxillary and mandibular teeth, with the crowns of both dental arch being detectable. Coronal part of the tooth had to be in toto visible on the radiograph and that no structure is projected on the examined tooth, teeth with overlap free projection selected
- Periapical Radiographs: patients over 18 years of age that were clear and displayed the full anatomical crown and root apex.
Exclusion Criteria:
- Radiographs of deciduous teeth or mixed dentition, those with severe noise or haziness, radiographs of partially edentulous areas, teeth exhibiting abnormal crown or root anatomy, and any radiographs that were difficult to assess.
- Overlapping images, radiographs with distortions, noise and shadows, and low quality radiographs
- Radiographs excluded if at least 2-3 examiners did not agree.
Studieplan
Hur är studien utformad?
Designdetaljer
- Observationsmodeller: COHORT
- Tidsperspektiv: CROSS_SECTIONAL
Vad mäter studien?
Primära resultatmått
Primära resultatmått
Resultatmått |
Åtgärdsbeskrivning |
Tidsram |
|---|---|---|
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(ROCAUC)
Tidsram: 12 months
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Receiver-Operating-Characteristics curve |
12 months
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Sekundära resultatmått
Sekundära resultatmått
Resultatmått |
Åtgärdsbeskrivning |
Tidsram |
|---|---|---|
|
Diagnostic test parameters
Tidsram: 12 months
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Sensitivity, Specificity, PPV, NPV |
12 months
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Samarbetspartners och utredare
Sponsor
Sponsor
Utredare
Utredare
- Studierektor: Susan Hattar, PhD, University of Jordan
Publikationer och användbara länkar
Allmänna publikationer
- Mertens S, Krois J, Cantu AG, Arsiwala LT, Schwendicke F. Artificial intelligence for caries detection: Randomized trial. J Dent. 2021 Dec;115:103849. doi: 10.1016/j.jdent.2021.103849. Epub 2021 Oct 14.
- Schwendicke F, Tzschoppe M, Paris S. Radiographic caries detection: A systematic review and meta-analysis. J Dent. 2015 Aug;43(8):924-33. doi: 10.1016/j.jdent.2015.02.009. Epub 2015 Feb 24.
- Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, Elhennawy K, Schwendicke F. Detecting caries lesions of different radiographic extension on bitewings using deep learning. J Dent. 2020 Sep;100:103425. doi: 10.1016/j.jdent.2020.103425. Epub 2020 Jul 4.
- Putra RH, Doi C, Yoda N, Astuti ER, Sasaki K. Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofac Radiol. 2022 Jan 1;51(1):20210197. doi: 10.1259/dmfr.20210197. Epub 2021 Jul 8.
- Kuhnisch J, Meyer O, Hesenius M, Hickel R, Gruhn V. Caries Detection on Intraoral Images Using Artificial Intelligence. J Dent Res. 2022 Feb;101(2):158-165. doi: 10.1177/00220345211032524. Epub 2021 Aug 20.
Studieavstämningsdatum
Studera stora datum
Studiestart (Beräknad)
Studiestart
Primärt slutförande (Beräknad)
Primärt slutförande
Avslutad studie (Beräknad)
Avslutad studie
Studieregistreringsdatum
Först inskickad
Först inskickad
Först inskickad som uppfyllde QC-kriterierna
Först inskickad som uppfyllde QC-kriterierna
Första postat (Faktisk)
Första postat
Uppdateringar av studier
Senaste uppdatering publicerad (Faktisk)
Senaste uppdatering publicerad
Senaste inskickade uppdateringen som uppfyllde QC-kriterierna
Senaste inskickade uppdateringen som uppfyllde QC-kriterierna
Senast verifierad
Senast verifierad
Mer information
Termer relaterade till denna studie
Ytterligare relevanta MeSH-villkor
Andra studie-ID-nummer
Andra studie-ID-nummer
- ToothVision
- IRB (Annan identifierare: IRB Jordan University Hospital)
Plan för individuella deltagardata (IPD)
Planerar du att dela individuella deltagardata (IPD)?
Läkemedels- och apparatinformation, studiedokument
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