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Review
. 2020 Jul;9(4):186-193.
doi: 10.1159/000504390. Epub 2019 Dec 4.

Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis

Lei Xu  1   2 , Junling Gao  1 , Quan Wang  3 , Jichao Yin  2 , Pengfei Yu  4 , Bin Bai  4 , Ruixia Pei  2 , Dingzhang Chen  4 , Guochun Yang  2 , Shiqi Wang  4 , Mingxi Wan  1
Affiliations
Review

Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis

Lei Xu et al. Eur Thyroid J. 2020 Jul.

Abstract

Background: Computer-aided diagnosis (CAD) systems are being applied to the ultrasonographic diagnosis of malignant thyroid nodules, but it remains controversial whether the systems add any accuracy for radiologists.

Objective: To determine the accuracy of CAD systems in diagnosing malignant thyroid nodules.

Methods: PubMed, EMBASE, and the Cochrane Library were searched for studies on the diagnostic performance of CAD systems. The diagnostic performance was assessed by pooled sensitivity and specificity, and their accuracy was compared with that of radiologists. The present systematic review was registered in PROSPERO (CRD42019134460).

Results: Nineteen studies with 4,781 thyroid nodules were included. Both the classic machine learning- and the deep learning-based CAD system had good performance in diagnosing malignant thyroid nodules (classic machine learning: sensitivity 0.86 [95% CI 0.79-0.92], specificity 0.85 [95% CI 0.77-0.91], diagnostic odds ratio (DOR) 37.41 [95% CI 24.91-56.20]; deep learning: sensitivity 0.89 [95% CI 0.81-0.93], specificity 0.84 [95% CI 0.75-0.90], DOR 40.87 [95% CI 18.13-92.13]). The diagnostic performance of the deep learning-based CAD system was comparable to that of the radiologists (sensitivity 0.87 [95% CI 0.78-0.93] vs. 0.87 [95% CI 0.85-0.89], specificity 0.85 [95% CI 0.76-0.91] vs. 0.87 [95% CI 0.81-0.91], DOR 40.12 [95% CI 15.58-103.33] vs. DOR 44.88 [95% CI 30.71-65.57]).

Conclusions: The CAD systems demonstrated good performance in diagnosing malignant thyroid nodules. However, experienced radiologists may still have an advantage over CAD systems during real-time diagnosis.

Keywords: Artificial intelligence; Thyroid nodule; Ultrasonography.

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Conflict of interest statement

The authors have no conflicts of interest to declare.

Figures

Fig. 1
Fig. 1
Search, inclusion, and exclusion flow diagram.
Fig. 2
Fig. 2
Forest plots of computer-aided diagnosis (CAD) systems and the radiologist counterparts. The sensitivity and specificity of the individual studies are represented by gray squares, and the pooled results are represented by rhombi. The confidence interval (CI) is indicated by error bars. a Diagnostic performance of classic machine learning-based CAD systems: AUC 0.93 (95% CI 0.90–0.95) and DOR 37.41 (95% CI 24.91–56.20). b Diagnostic performance of deep learning-based CAD systems: AUC 0.93 (95% CI 0.90–0.95) and DOR 40.87 (95% CI 18.13–92.13). c, d Comparison between deep learning-based CAD systems (c) and radiologists (d): AUC 0.93 (95% CI 0.90–0.95) versus 0.92 (95% CI 0.89–0.94) and DOR 40.12 (95% CI 15.58–103.33) versus 44.88 (95% CI 30.71–65.57). e, f Comparison between deep learning-based real-time CAD systems (e) and radiologists (f): AUC 0.88 (95% CI 0.85–0.91) versus 0.88 (95% CI 0.85–0.91) and DOR 19.82 (95% CI 5.92–66.35) versus 55.93 (95% CI 17.72–176.54).
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