DiagnosisNCT07908017
Diagnostic Performance of Ai vs Junior Dentists
An observational study of Diagnosis, run by University of Jordan at 1 site in Jordan.
Trial 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 more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Diagnosis
- How many people
- 120 participants
- Where
- 1 siteJordan
- Study dates
- Nov 2026to Oct 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Patients over 18 years of age that were clear and displayed the full anatomical crown and root apex.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: 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.
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?
- Conditions
- Diagnosis
- Medical subject headings
- Disease
What is being measured
The primary outcome is the main question the study is designed to answer.
(ROCAUC)
Receiver-Operating-Characteristics curve
Measured: 12 months
Secondary outcomes (1)
Diagnostic test parameters
Sensitivity, Specificity, PPV, NPV
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Oct 2026
- Study startNov 2026 (estimated)
- Primary completionJul 2027 (estimated)
- Study completionOct 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for DiagnosisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 120 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Jordan
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Susan Hattar, University of Jordan
- Investigators
- Susan Hattar, PhD, Study director, University of Jordan
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07908017
- Sponsor's protocol ID
- ToothVision
- Other IDs
- IRB
- Status last verified
- Oct 2026
- First submitted
- 24 Oct 2026
- Last updated
- 30 Oct 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
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 34656656Putra 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 34233515Kuhnisch 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 34416824Schwendicke 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 25724114Cantu 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
Study at a glance
From the registry record, last updated by the sponsor on 30 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparison of Artificial Intelligence vs. Junior Dentists Diagnostic Performance for Radiographic Caries & Periapical Lesion Detection
- ClinicalTrials.gov ID
- NCT07908017
- Acronym / sponsor ID
- ToothVision
- Conditions
- Diagnosis
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 120 participants (estimated)
- Lead sponsor
- University of JordanUniversity, hospital or other
- Study start
- Nov 2026 (estimated)
- Primary completion
- Jul 2027 (estimated)
- Study completion
- Oct 2027 (estimated)
- First posted
- 30 Oct 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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?
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients over 18 years of age that were clear and displayed the full anatomical crown and root apex.
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
(ROCAUC)
Time frame: 12 months
Receiver-Operating-Characteristics curve
1 secondary outcome
Diagnostic test parameters
Time frame: 12 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Oct 2026.
- Background: PubMed 34656656Mertens 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.
- Background: PubMed 34233515Putra 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.
- Background: PubMed 34416824Kuhnisch 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.
- Results publication: PubMed 25724114Schwendicke 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.
- Results publication: PubMed 32634466Cantu 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.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Susan Hattar, PhD study director
University of Jordan
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Diagnosis, run by University of Jordan at 1 site in Jordan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 30 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Jordan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Susan Hattar, PhD (+962795642613, s.hattar@ju.edu.jo), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
(ROCAUC) (time frame: 12 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07908017, which also lists 5 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Oct 2026.