Evidence map›Paper›PMID 39926973›Full record

ReviewJournal of pharmacy & bioallied sciences2024

Developing an AI-Powered Algorithm for Automated Detection and Classification of Dental Caries from Intraoral Radiographs: A Machine Learning Approach.

Mohammad Haider

Abstract readReview
In one paragraph

Review in Journal of pharmacy & bioallied sciences, 2024. 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

1 author.

Mohammad HaiderDepartment of Dentistry, Margalla Institute of Health Sciences, Quaid Azam Avenue gulrez -3 Rawalpindi, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This systematic review aims to assess the diagnostic accuracy of artificial intelligence (AI) models specifically developed for detecting and classifying dental caries. A comprehensive electronic literature search was performed on the PubMed, Web of Science, SCOPUS, and Embase databases. The search yielded a total of 397 results. We examined 10 articles that satisfied the selection criteria. In summary, AI-based models show high diagnostic accuracy in recognizing dental caries by analyzing dental radiography pictures.

Indexed as

Artificial intelligenceclassificationdental cariesdetection

Identifiers

PMID39926973
PMCPMC11805296

What OpenQuestion holds

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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.