Evidence map›Paper›PMID 33198554›Full record

ArticleJournal of dental research2021

Cost-effectiveness of Artificial Intelligence for Proximal Caries Detection.

F Schwendicke, J G Rossi, G Göstemeyer, K Elhennawy, A G Cantu, R Gaudin, A Chaurasia, S Gehrung, J Krois

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In one paragraph

Article in Journal of dental research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed, 6 pooled it
14.7field-weighted citation impact, top 1% of its field
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

64 citing papers in PubMed, 6 syntheses or guidelines pooled it, 130 citations in OpenAlex.

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4 more citing papers are in PubMed but not listed here.

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

9 authors at 2 institutions in 2 countries.

F SchwendickeDepartment of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Berlin, Germany.ORCID 0000-0003-1223-1669
J G RossiDepartment of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Berlin, Germany.
G GöstemeyerDepartment of Operative and Preventive Dentistry, Charité-Universitätsmedizin Berlin, Berlin, Germany.
K ElhennawyDepartment of Orthodontics, Dentofacial Orthopedics and Pedodontics, Charité-Universitätsmedizin Berlin, Berlin, Germany.
A G CantuDepartment of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Berlin, Germany.
R GaudinDepartment of Oral and Maxillofacial Surgery, Charité-Universitätsmedizin Berlin, Berlin, Germany.
A ChaurasiaDepartment of Oral Medicine and Radiology, King George's Medical University, Lucknow, India.
S GehrungDepartment of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Berlin, Germany.
J KroisDepartment of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Charité - Universitätsmedizin Berlin · DEKing George's Medical University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) can assist dentists in image assessment, for example, caries detection. The wider health and cost impact of employing AI for dental diagnostics has not yet been evaluated. We compared the cost-effectiveness of proximal caries detection on bitewing radiographs with versus without AI. U-Net, a fully convolutional neural network, had been trained, validated, and tested on 3,293, 252, and 141 bitewing radiographs, respectively, on which 4 experienced dentists had marked carious lesions (reference test). Lesions were stratified for initial lesions (E1/E2/D1, presumed noncavitated, receiving caries infiltration if detected) and advanced lesions (D2/D3, presumed cavitated, receiving restorative care if detected). A Markov model was used to simulate the consequences of true- and false-positive and true- and false-negative detections, as well as the subsequent decisions over the lifetime of patients. A German mixed-payers perspective was adopted. Our health outcome was tooth retention years. Costs were measured in 2020 euro. Monte-Carlo microsimulations and univariate and probabilistic sensitivity analyses were conducted. The incremental cost-effectiveness ratio (ICER) and the cost-effectiveness acceptability at different willingness-to-pay thresholds were quantified. AI showed an accuracy of 0.80; dentists' mean accuracy was significantly lower at 0.71 (minimum-maximum: 0.61-0.78,

Indexed as

Dental CariesDental Caries SusceptibilityArtificial IntelligenceCost-Benefit AnalysisHumansMonte Carlo Methodcaries diagnosis/preventioncomputer simulationdecision makingdentaleconomic evaluationradiology

Identifiers

PMID33198554
PMCPMC7985854
OpenAlexW3100431970

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.