In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 3 ( 2023-3-23), p. e0273445-
Abstract:
Lung cancer is a common malignant tumor disease with high clinical disability and death rates. Currently, lung cancer diagnosis mainly relies on manual pathology section analysis, but the low efficiency and subjective nature of manual film reading can lead to certain misdiagnoses and omissions. With the continuous development of science and technology, artificial intelligence (AI) has been gradually applied to imaging diagnosis. Although there are reports on AI-assisted lung cancer diagnosis, there are still problems such as small sample size and untimely data updates. Therefore, in this study, a large amount of recent data was included, and meta-analysis was used to evaluate the value of AI for lung cancer diagnosis. With the help of STATA16.0, the value of AI-assisted lung cancer diagnosis was assessed by specificity, sensitivity, negative likelihood ratio, positive likelihood ratio, diagnostic ratio, and plotting the working characteristic curves of subjects. Meta-regression and subgroup analysis were used to investigate the value of AI-assisted lung cancer diagnosis. The results of the meta-analysis showed that the combined sensitivity of the AI-aided diagnosis system for lung cancer diagnosis was 0.87 [95% CI (0.82, 0.90)] , specificity was 0.87 [95% CI (0.82, 0.91)] (CI stands for confidence interval.), the missed diagnosis rate was 13%, the misdiagnosis rate was 13%, the positive likelihood ratio was 6.5 [95% CI (4.6, 9.3)] , the negative likelihood ratio was 0.15 [95% CI (0.11, 0.21)], a diagnostic ratio of 43 [95% CI (24, 76)] and a sum of area under the combined subject operating characteristic (SROC) curve of 0.93 [95% CI (0.91, 0.95)]. Based on the results, the AI-assisted diagnostic system for CT (Computerized Tomography), imaging has considerable diagnostic accuracy for lung cancer diagnosis, which is of significant value for lung cancer diagnosis and has greater feasibility of realizing the extension application in the field of clinical diagnosis.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0273445
DOI:
10.1371/journal.pone.0273445.g001
DOI:
10.1371/journal.pone.0273445.g002
DOI:
10.1371/journal.pone.0273445.g003
DOI:
10.1371/journal.pone.0273445.g004
DOI:
10.1371/journal.pone.0273445.g005
DOI:
10.1371/journal.pone.0273445.g006
DOI:
10.1371/journal.pone.0273445.g007
DOI:
10.1371/journal.pone.0273445.g008
DOI:
10.1371/journal.pone.0273445.t001
DOI:
10.1371/journal.pone.0273445.t002
DOI:
10.1371/journal.pone.0273445.t003
DOI:
10.1371/journal.pone.0273445.t004
DOI:
10.1371/journal.pone.0273445.t005
DOI:
10.1371/journal.pone.0273445.s001
DOI:
10.1371/journal.pone.0273445.s002
DOI:
10.1371/journal.pone.0273445.s003
DOI:
10.1371/journal.pone.0273445.s004
DOI:
10.1371/journal.pone.0273445.s005
DOI:
10.1371/journal.pone.0273445.r001
DOI:
10.1371/journal.pone.0273445.r002
DOI:
10.1371/journal.pone.0273445.r003
DOI:
10.1371/journal.pone.0273445.r004
DOI:
10.1371/journal.pone.0273445.r005
DOI:
10.1371/journal.pone.0273445.r006
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2267670-3
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