Evidence map›Paper›PMID 40445547›Full record

ArticleBulletin of experimental biology and medicine2025

Diagnostic Efficiency of an Artificial Intelligence-Based Technology in Dental Radiography.

A A Obrubov, E A Solovykh, A G Nadtochiy

Abstract read
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In one paragraph

Article in Bulletin of experimental biology and medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

A A ObrubovCentral Research Institute of Dentistry and Maxillofacial Surgery, Ministry of Health of the Russian Federation, Moscow, Russia. dr.obrubov@yandex.ru.
E A SolovykhLaboratory of Functional Diagnostics LLC, Moscow, Russia.
A G NadtochiyCentral Research Institute of Dentistry and Maxillofacial Surgery, Ministry of Health of the Russian Federation, Moscow, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present results of the development of Dentomo artificial intelligence model based on two neural networks. The model includes a database and a knowledge base harmonized with SNOMED CT that allows processing and interpreting the results of cone beam computed tomography (CBCT) scans of the dental system, in particular, identifying and classifying teeth, identifying CT signs of pathology and previous treatments. Based on these data, artificial intelligence can draw conclusions and generate medical reports, systematize the data, and learn from the results. The diagnostic effectiveness of Dentomo was evaluated. The first results of the study have demonstrated that the model based on neural networks and artificial intelligence is a valuable tool for analyzing CBCT scans in clinical practice and optimizing the dentist workflow.

Indexed as

Artificial IntelligenceCone-Beam Computed TomographyRadiography, DentalToothHumansNeural Networks, Computerartificial intelligencecone-beam computed tomographydentistry radiologyinformation technology

Identifiers

PMID40445547

What OpenQuestion holds

Textmetadata
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.