Computer-aided diagnosis system for thyroid nodules on ultrasonography: diagnostic performance and reproducibility based on the experience level of operators

Jeong, Eun Young; Kim, Hye Lin; Ha, Eun Ju; Park, Seon-Young; Cho, Yoon Joo; Han, Miran

doi:10.1007/s00330-018-5772-9

Cited by 79 publications

(88 citation statements)

References 14 publications

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“…The pooled sensitivity, specificity, AUC, and DOR are demonstrated in Figure 2b. Eleven of the 13 studies compared the diagnostic performances of CAD systems and radiologists [7,20,21,[23][24][25][26][27][28]. The pooled sensitivity, specificity, AUC, and DOR were comparable between the CAD systems and the radiologists (Fig.…”

Section: Diagnostic Performance Of Deep Learning-based Cad Systemsmentioning

confidence: 94%

“…The quality of the included studies is summarized in online supplementary Table 2. The risk of bias from patient selection was judged to be high or unclear in 13 of the included studies: 4 studies limited the nodule size within a certain scope [16,17,21,25]; 5 studies excluded difficult-to-diagnose nodules [15,[25][26][27]31]; and 4 studies were unclear about whether there were selected co-horts and inappropriate exclusions [14,19,23,29]. The risk of bias from the reference standard was considered to be unclear in 2 of the included studies [14,23].…”

Section: Methodological Quality Of the Included Studiesmentioning

confidence: 99%

“…The risk of bias from the reference standard was considered to be unclear in 2 of the included studies [14,23]. The risk of bias from flow and timing was considered to be high or unclear in 7 of the included studies, and these studies adopted pathological examination, fine needle aspiration, and ultrasound as reference standards for diagnosing benign nodules [19,22,25,27,28,30,31].…”

Section: Methodological Quality Of the Included Studiesmentioning

confidence: 99%

“…The flow diagram of the literature search is shown in Figure 1. Nineteen studies with 4,781 nodules used in external validation sets were included in the study, including 6 studies on classic machine learning-based CAD sys-DOI: 10.1159/000504390 tems [14][15][16][17][18][19] and 13 studies on deep learning-based CAD systems [7,[20][21][22][23][24][25][26][27][28][29][30][31]. The general characteristics of the included studies are shown in Table 1, and the detailed characteristics are demonstrated in online supplementary Table 1 (see www.karger.com/doi/10.1159/000504390 for all online suppl.…”

Section: Literature Searches and Description Of Studiesmentioning

confidence: 99%

“…Thirteen studies with 1,667 malignant and 1,415 benign nodules were included in the analysis [7,[20][21][22][23][24][25][26][27][28][29][30][31]. The pooled sensitivity, specificity, AUC, and DOR are demonstrated in Figure 2b.…”

Section: Diagnostic Performance Of Deep Learning-based Cad Systemsmentioning

confidence: 99%

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

Xu¹,

Gao²,

Wang³

et al. 2019

Eur Thyroid J

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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.

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Section: Diagnostic Performance Of Deep Learning-based Cad Systemsmentioning

confidence: 94%

Section: Methodological Quality Of the Included Studiesmentioning

confidence: 99%

Section: Methodological Quality Of the Included Studiesmentioning

confidence: 99%

Section: Literature Searches and Description Of Studiesmentioning

confidence: 99%

Section: Diagnostic Performance Of Deep Learning-based Cad Systemsmentioning

confidence: 99%

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

Xu¹,

Gao²,

Wang³

et al. 2019

Eur Thyroid J

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Diagnostic Value of Artificial Intelligence‐Assistant Diagnostic System Combined With Contrast‐Enhanced Ultrasound in Thyroid TI‐RADS 4 Nodules

Gong

Xin

Yin

et al. 2023

J of Ultrasound Medicine

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Objectives This study evaluated the diagnostic value of artificial intelligence‐assistant diagnostic system combined with contrast‐enhanced ultrasound in The American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI‐RADS) 4 category thyroid nodules. Methods Thyroid nodules that were evaluated as ACR TI‐RADS 4 by conventional ultrasound were selected, all of which had pathological or fine needle aspiration (FNA) results. All nodules were examined by contrast‐enhanced ultrasound (CEUS) and artificial intelligence (AI) analysis. The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of AI, CEUS and their combined diagnosis were compared; Analyzed and compared the diagnostic efficiency of AI, CEUS and their combined diagnosis. Results A total of 148 thyroid nodules were included in 140 patients, including 58 malignant nodules and 89 benign nodules. The sensitivity of combined diagnosis was significantly higher than that of AI or CEUS alone (P < .05). The NPV of AI, CEUS and combined diagnosis were statistically significant (P < .05). There was no significant difference in the diagnostic efficacy between AI and CEUS (P > .05), but there was a significant difference in NPV between AI and combined diagnosis (P < .05). The AUC of the combined diagnosis was 0.859, which was higher than that of AI, CEUS alone. Conclusions AI has a high diagnostic efficiency, which was helpful for radiologists to make rapid assessment. AI combined CEUS can significantly improve the diagnostic sensitivity and NPV, which was beneficial for the early detection of malignant nodules.

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Clinical Value of Artificial Intelligence‐Based Computer‐Aided Diagnosis System Versus Contrast‐Enhanced Ultrasound for Differentiation of Benign From Malignant Thyroid Nodules in Different Backgrounds

Liu

Wang

et al. 2023

J of Ultrasound Medicine

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Objectives The aim of this study was to compare the value of the AI‐SONIC ultrasound‐assisted diagnosis system versus contrast‐enhanced ultrasound (CEUS) for differential diagnosis of thyroid nodules in diffuse and non‐diffuse backgrounds. Methods A total of 555 thyroid nodules with pathologically confirmed diagnosis were included in this retrospective study. The diagnostic efficacies of AI‐SONIC and CEUS for differentiating benign from malignant nodules in diffuse and non‐diffuse backgrounds were evaluated, with pathological diagnosis as the gold standard. Results The agreement between AI‐SONIC diagnosis and pathological diagnosis was moderate in diffuse backgrounds (κ = 0.417) and almost perfect in non‐diffuse backgrounds (κ = 0.81). The agreement between CEUS diagnosis and pathological diagnosis was substantial in diffuse backgrounds (κ = 0.684) and moderate in non‐diffuse backgrounds (κ = 0.407). In diffuse backgrounds, AI‐SONIC had slightly higher sensitivity (95.7 vs 89.4%, P = .375), but CEUS had significantly higher specificity (80.0 vs 40.0%, P = .008). In non‐diffuse background, AI‐SONIC had significantly higher sensitivity (96.2 vs 73.4%, P < .001), specificity (82.9 vs 71.2%, P = .007), and negative predictive value (90.3 vs 53.3%, P < .001). Conclusion In non‐diffuse backgrounds, AI‐SONIC is superior to CEUS for differentiating malignant from benign thyroid nodules. In diffuse backgrounds, AI‐SONIC could be useful for screening of cases to detect suspicious nodules requiring further examination by CEUS.

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Computer-aided diagnosis system for thyroid nodules on ultrasonography: diagnostic performance and reproducibility based on the experience level of operators

Cited by 79 publications

References 14 publications

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

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

Diagnostic Value of Artificial Intelligence‐Assistant Diagnostic System Combined With Contrast‐Enhanced Ultrasound in Thyroid TI‐RADS 4 Nodules

Clinical Value of Artificial Intelligence‐Based Computer‐Aided Diagnosis System Versus Contrast‐Enhanced Ultrasound for Differentiation of Benign From Malignant Thyroid Nodules in Different Backgrounds

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