Evidence map›Paper›PMID 34268376›Full record

ArticleAnnals of translational medicine2021

Artificial intelligence in digital cariology: a new tool for the diagnosis of deep caries and pulpitis using convolutional neural networks.

Liwen Zheng, Haolin Wang, Li Mei, Qiuman Chen, Yuxin Zhang, Hongmei Zhang

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Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 4 pooled it
–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

34 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  15. Artificial Intelligence in Dentistry: A Descriptive Review.Bioengineering (Basel, Switzerland) · 2024
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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

6 authors.

Liwen ZhengChongqing Key Laboratory for Oral Diseases and Biomedical Sciences, the Affiliated Hospital of Stomatology of Chongqing Medical University, Chongqing, China.
Haolin WangCollege of Medical Informatics, Chongqing Medical University, Chongqing, China.
Li MeiDepartment of Oral Sciences, Faculty of Dentistry, University of Otago, Dunedin, New Zealand.
Qiuman ChenChongqing Key Laboratory for Oral Diseases and Biomedical Sciences, the Affiliated Hospital of Stomatology of Chongqing Medical University, Chongqing, China.
Yuxin ZhangChongqing Key Laboratory for Oral Diseases and Biomedical Sciences, the Affiliated Hospital of Stomatology of Chongqing Medical University, Chongqing, China.
Hongmei ZhangChongqing Key Laboratory for Oral Diseases and Biomedical Sciences, the Affiliated Hospital of Stomatology of Chongqing Medical University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAn accurate diagnosis of deep caries and pulpitis on periapical radiographs is a clinical challenge.

methodsA total of 844 radiographs were included in this study. Of the 844, 717 (85%) were used for training and 127 (15%) were used for testing the three convolutional neural networks (CNNs) (VGG19, Inception V3, and ResNet18). The performance [accuracy, precision, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC)] of the CNNs were evaluated and compared. The CNN model with the best performance was further integrated with clinical parameters to see whether multi-modal CNN could provide an enhanced performance. The Gradient-weighted Class Activation Mapping (Grad-CAM) technique illustrates what image feature was the most important for the CNNs.

resultsThe CNN of ResNet18 demonstrated the best performance [accuracy =0.82, 95% confidence interval (CI): 0.80-0.84; precision =0.81, 95% CI: 0.73-0.89; sensitivity =0.85, 95% CI: 0.79-0.91; specificity =0.82, 95% CI: 0.76-0.88; and AUC =0.89, 95% CI: 0.86-0.92], compared with VGG19 and Inception V3 as well as the comparator dentists. Therefore, ResNet18 was chosen to integrate with clinical parameters to produce the multi-modal CNN of ResNet18 + C, which showed a significantly enhanced performance (accuracy =0.86, 95% CI: 0.84-0.88; precision =0.85, 95% CI: 0.76-0.94; sensitivity =0.89, 95% CI: 0.83-0.95; specificity =0.86, 95% CI: 0.79-0.93; and AUC =0.94, 95% CI: 0.91-0.97).

conclusionsThe CNN of ResNet18 showed good performance (accuracy, precision, sensitivity, specificity, and AUC) for the diagnosis of deep caries and pulpitis. The multi-modal CNN of ResNet18 + C (ResNet18 integrated with clinical parameters) demonstrated a significantly enhanced performance, with promising potential for the diagnosis of deep caries and pulpitis.

Indexed as

Artificial intelligence (AI)cariescarious lesionsconvolutional neural network (CNNs)deep learningpulpitis

Identifiers

PMID34268376
PMCPMC8246233

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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.