Evidence map›Paper›PMID 40025477›Full record

ArticleBMC oral health2025

Evaluation by dental professionals of an artificial intelligence-based application to measure alveolar bone loss.

Sang Won Lee, Kateryna Huz, Kayla Gorelick, Jackie Li, Thomas Bina, Satoko Matsumura, Noah Yin, Nicholas Zhang, Yvonne Naa Ardua Anang, Sanam Sachadava and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Sang Won LeeDepartment of Biomedical Engineering, Columbia University, New York, NY, 10027, USA. sl4447@columbia.edu.ORCID http://orcid.org/0000-0003-2948-4099
Kateryna HuzDivision of Orthodontics, Columbia University College of Dental Medicine, New York, NY, 10032, USA.
Kayla GorelickDivision of Orthodontics, Columbia University College of Dental Medicine, New York, NY, 10032, USA.
Jackie LiDepartment of Biomedical Engineering, Columbia University, New York, NY, 10027, USA.
Thomas BinaDepartment of Biomedical Engineering, Columbia University, New York, NY, 10027, USA.
Satoko MatsumuraDivision of Oral & Maxillofacial Radiology, Columbia University College of Dental Medicine, New York, NY, 10032, USA.
Noah YinDepartment of Biomedical Engineering, Columbia University, New York, NY, 10027, USA.
Nicholas ZhangDepartment of Biomedical Engineering, Columbia University, New York, NY, 10027, USA.
Yvonne Naa Ardua AnangDepartment of Biomedical Engineering, Columbia University, New York, NY, 10027, USA.
Sanam SachadavaDivision of Orthodontics, Columbia University College of Dental Medicine, New York, NY, 10032, USA.
Helena I Servin-DeMarraisDepartment of Biomedical Engineering, Columbia University, New York, NY, 10027, USA.
Donald J McMahonDivision of Oral & Maxillofacial Radiology, Columbia University College of Dental Medicine, New York, NY, 10032, USA.
Helen H Lu *Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA.
Michael T Yin *Vagelos College of Physicians and Surgeons, Division of Infectious Diseases, Columbia University, New York, NY, 10032, USA.ORCID http://orcid.org/0000-0002-9346-9056
Sunil Wadhwa *Division of Orthodontics, Columbia University College of Dental Medicine, New York, NY, 10032, USA.ORCID http://orcid.org/0000-0001-5743-9231

Funding

Mentoring Research in Non-infectious Complications of HIVK24AI155230 · NIAID · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI YIN, MICHAEL T · 2021 to 2025
$940k
NIAID NIH HHS K24 AI155230
6 · The paper itself

Abstract

backgroundSeveral commercial programs incorporate artificial intelligence in diagnosis, but very few dental professionals have been surveyed regarding its acceptability and usability. Furthermore, few have explored how these advances might be incorporated into routine practice.

methodsOur team developed and implemented a deep learning (DL) model employing semantic segmentation neural networks and object detection networks to precisely identify alveolar bone crestal levels (ABCLs) and cemento-enamel junctions (CEJs) to measure change in alveolar crestal height (ACH). The model was trained and validated using a 550 bitewing radiograph dataset curated by an oral radiologist, setting a gold standard for ACH measurements. A twenty-question survey was created to compare the accuracy and efficiency of manual X-ray examination versus the application and to assess the acceptability and usability of the application.

resultsIn total, 56 different dental professionals classified severe (ACH > 5 mm) vs. non-severe (ACH ≤ 5 mm) periodontal bone loss on 35 calculable ACH measures. Dental professionals accurately identified between 35-87% of teeth with severe periodontal disease, whereas the artificial intelligence (AI) application achieved an 82-87% accuracy rate. Among the 65 participants who completed the acceptability and usability survey, more than half the participants (52%) were from an academic setting. Only 21% of participants reported that they already used automated or AI-based software in their practice to assist in reading of X-rays. The majority, 57%, stated that they only approximate when measuring bone levels and only 9% stated that they measure with a ruler. The survey indicated that 84% of participants agreed or strongly agreed with the AI application measurement of ACH. Furthermore, 56% of participants agreed that AI would be helpful in their professional setting.

conclusionOverall, the study demonstrates that an AI application for detecting alveolar bone has high acceptability among dental professionals and may provide benefits in time saving and increased clinical accuracy.

Indexed as

Alveolar Bone LossArtificial IntelligenceDentistsAdultDeep LearningFemaleHumansMaleNeural Networks, ComputerAcceptabilityAlveolar crestal heightArtificial intelligenceDeep learningDental professionalsPeriodontal disease

Identifiers

PMID40025477
PMCPMC11872301

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.