Evidence map›Paper›PMID 30004241›Full record

ArticleDento maxillo facial radiology2019

Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study.

Jae-Seo Lee, Shyam Adhikari, Liu Liu, Ho-Gul Jeong, Hyongsuk Kim, Suk-Ja Yoon

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Dento maxillo facial radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 5 pooled it
13.4field-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

63 citing papers in PubMed, 5 syntheses or guidelines pooled it, 167 citations in OpenAlex.

  1. Pooled it
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  3. Diagnostic accuracy of artificial intelligence models in detecting osteoporosis using dental images: a systematic review and meta-analysis.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025
    Pooled it
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  5. Application of artificial intelligence in diagnosis of osteoporosis using medical images: a systematic review and meta-analysis.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2021
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3 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

6 authors at 2 institutions in 1 country.

Jae-Seo LeeDepartment of Oral and Maxillofacial Radiology, School of Dentistry, Dental Science Research Institute, Chonnam National University, Gwangju, South Korea.
Shyam AdhikariDivision of Electronics Engineering, Chonbuk National University, Jeonju, South Korea.
Liu LiuDepartment of Oral and Maxillofacial Radiology, School of Dentistry, Dental Science Research Institute, Chonnam National University, Gwangju, South Korea.
Ho-Gul JeongDental Imaging Research Center, Medipartner, Seoul, South Korea.
Hyongsuk KimDivision of Electronics Engineering, Chonbuk National University, Jeonju, South Korea.
Suk-Ja YoonDepartment of Oral and Maxillofacial Radiology, School of Dentistry, Dental Science Research Institute, Chonnam National University, Gwangju, South Korea.
Chonnam National University · KRJeonbuk National University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo evaluate the diagnostic performance of a deep convolutional neural network (DCNN)-based computer-assisted diagnosis (CAD) system in the detection of osteoporosis on panoramic radiographs, through a comparison with diagnoses made by oral and maxillofacial radiologists.

methodsOral and maxillofacial radiologists with >10 years of experience reviewed the panoramic radiographs of 1268 females {mean [± standard deviation (SD)] age: 52.5 ± 22.3 years} and made a diagnosis of osteoporosis when cortical erosion of the mandibular inferior cortex was observed. Among the females, 635 had no osteoporosis [mean (± SD) age: 32.8 ± SD 12.1 years] and 633 had osteoporosis (72.2 ± 8.5 years). All panoramic radiographs were analysed using three CAD systems, single-column DCNN (SC-DCNN), single-column with data augmentation DCNN (SC-DCNN Augment) and multicolumn DCNN (MC-DCNN). Among the radiographs, 200 panoramic radiographs [mean (± SD) patient age: 63.9 ± 10.7 years] were used for testing the performance of the DCNN in detecting osteoporosis in this study. The diagnostic performance of the DCNN-based CAD system was assessed by receiver operating characteristic (ROC) analysis.

resultsThe area under the curve (AUC) values obtained using SC-DCNN, SC-DCNN (Augment) and MC-DCNN were 0.9763, 0.9991 and 0.9987, respectively.

conclusionsThe DCNN-based CAD system showed high agreement with experienced oral and maxillofacial radiologists in detecting osteoporosis. A DCNN-based CAD system could provide information to dentists for the early detection of osteoporosis, and asymptomatic patients with osteoporosis can then be referred to the appropriate medical professionals.

Indexed as

Neural Networks, ComputerOsteoporosisRadiography, PanoramicAdultAgedChildDiagnosis, Computer-AssistedFemaleHumansMiddle AgedROC Curve

Identifiers

PMID30004241
PMCPMC6398904
OpenAlexW2883574295

What OpenQuestion holds

Textmetadata
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Registered trials

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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.