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Not yet recruitingNCT07908017Updated Oct 30, 2026

Diagnostic Performance of Ai vs Junior Dentists

An observational study in Diagnosis, sponsored by University of Jordan. Not yet recruiting at 1 site in Jordan. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-10-30.

Primary conditionnone(registry's first term: Disease)No conditionIs this wrong?Disease describes something many studies share rather than a condition a study is about. The study names no condition we can place it under, so it is not compared with other studies by condition.

Sponsored by University of Jordan · Observational

Updated Oct 30, 2026Newly registeredGo to Updates ↓
Study type
Observational
Model
Cohort
Time perspective
Cross-sectional
Enrollment
120
Ages
18 Years and older
Sex
All
01

Study summary

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.
Read the detailed description

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 (Aloufi et al 2025) The performance of AI in caries detection remains moderate, and its periapical status assessment is insufficient due to high false-positive rates (Keskin et al 2025)

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?
02

Conditions studied

  • Diagnosis

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03

In context

Lead sponsor

University of Jordan is the lead sponsor of 131 studies on the registry; 20 are open to participants now.

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Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years and older
Sexes eligible
All
Accepts healthy volunteers
Yes
Sampling method
Probability sample

Study population

Patients over 18 years of age that were clear and displayed the full anatomical crown and root apex.

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

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

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.
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Study design

Observational model
Cohort
Time perspective
Cross-sectional
Enrollment
120 participants (estimated)
Patient registry
No
06

What researchers measure

Primary outcomes

  1. (ROCAUC)

    Receiver-Operating-Characteristics curve

    Time frame: 12 months

Secondary outcomes

  1. Diagnostic test parameters

    Sensitivity, Specificity, PPV, NPV

    Time frame: 12 months

07

Study locations

1 site
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References and documents

Publications

  • 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. PubMed 34656656 ↗
  • 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. PubMed 34233515 ↗
  • 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. PubMed 34416824 ↗
  • 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. PubMed 25724114 ↗
  • 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. PubMed 32634466 ↗

Individual participant data

Plan to share: Undecided

09

Timeline

1 registry update since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reached

Start is estimated for Nov 2026; primary completion for Jul 2027, 8 months later.

How these figures are worked out, and which studies are left out of them.

Registered
Oct 24, 2026
First posted
Oct 30, 2026
StartEst.
Nov 2026
Primary completionEst.
Jul 2027
CompletionEst.
Oct 2027
Last update
Oct 30, 2026
What these dates mean
Start.
When the first participant enrolled; while estimated, when the study expects to open to recruitment.
Primary completion.
When the last participant was examined or treated to collect data for the study's main question, its primary outcome. A study that stopped early gives the date its data collection stopped.
Completion.
When the last participant's last visit took place, for every outcome, secondary ones and side effects included: the end of the study. Often later than primary completion, sometimes the same day.
Actual and Est.
Actual means the registry says the date has happened. Est. (estimated) is a date the sponsor plans or expects. It can still move, and the sponsor is expected to change it to actual once it has passed.
Registered
First appeared on the registry. No changes since
Oct 30, 2026
Show all 1 update
  1. Oct 30, 2026
    First appeared on the registry

From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗

10

Registry details

Key details

Study ID
NCT07908017
Lead sponsor
University of Jordan
Responsible party
Susan Hattar (Professor, University of Jordan) — Principal investigator
First posted
Oct 30, 2026
Start date
Nov 2026 (estimated)
Primary completion
Jul 2027 (estimated)
Completion
Oct 2027 (estimated)
Last update
Oct 30, 2026

Study contacts

Susan Hattar, PhD
Contact
s.hattar@ju.edu.jo
+962795642613
Susan Hattar, PhD
study director · University of Jordan

Oversight

Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is not yet recruiting, as verified in Oct 2026. You cannot join it, but the record below documents what was studied.

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