Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound

Ko, Su Yeon; Lee, Ji Hye; Yoon, Jung Hyun; Na, Hyesun; Hong, Eunhye; Han, Kyunghwa; Jung, Inkyung; Kim, Min Jung; Moon, Hee Jung; Park, Vivian Youngjean; Lee, Eunjung; Kwak, Jin Young

doi:10.1002/hed.25415

Cited by 86 publications

(74 citation statements)

References 32 publications

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“…Second, the rigid cutoff levels that were adopted to determine the diagnostic conclusion of the radiologists may also have influenced the per-formance of the radiologists. For instance, points 4a, 4b, and 5 of the TI-RADS criteria were adopted by researchers to determine the conclusion of radiologists during the diagnostic process [7,21,24]. It is probable that a different conclusion would have been drawn if the cutoff level had been adjusted.…”

Section: Discussionmentioning

confidence: 99%

“…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 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: Discussionmentioning

confidence: 99%

Section: Diagnostic Performance Of Deep Learning-based Cad Systemsmentioning

confidence: 94%

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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Are All Deep Learning Architectures Alike for Point‐of‐Care Ultrasound?: Evidence From a Cardiac Image Classification Model Suggests Otherwise

Blaivas

2019

J of Ultrasound Medicine

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Objectives-Little is known about optimal deep learning (DL) approaches for point-of-care ultrasound (POCUS) applications. We compared 6 popular DL architectures for POCUS cardiac image classification to determine whether an optimal DL architecture exists for future DL algorithm development in POCUS.Methods-We trained 6 convolutional neural networks (CNNs) with a range of complexities and ages (AlexNet, VGG-16, VGG-19, ResNet50, Den-seNet201, and Inception-v4). Each CNN was trained by using images of 5 typical POCUS cardiac views. Images were extracted from 225 publicly available deidentified POCUS cardiac videos. A total of 750,018 individual images were extracted, with 90% used for model training and 10% for cross-validation. The training time and accuracy achieved were tracked. A real-world test of the algorithms was performed on a set of 125 completely new cardiac images. Descriptive statistics, Pearson R values, and κ values were calculated for each CNN.Results-Accuracy ranged from 96% to 85.6% correct for the 6 CNNs. VGG-16, one of the oldest and simplest CNNs, performed best at 96% correct with 232 minutes to train (R = 0.97; κ = 0.95; P < .00001). The worst-performing CNN was the newer DenseNet201, with 85.6% accuracy and 429 minutes to train (R = 0.92; κ = 0.82; P < .00001).Conclusions-Six common image classification DL algorithms showed considerable variability in their accuracy and training time when trained and tested on identical data, suggesting that not all will perform optimally for POCUS DL applications. Contrary to well-established accuracies for CNNs, more modern and deeper algorithms yielded poorer results.

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Study on the Consistency Between Automatic Measurement Based on Convolutional Neural Network Technology and Manual Visual Evaluation in Intracavitary Ultrasonic Cine of Anterior Pelvic

Wei,

Li,

et al. 2024

J of Ultrasound Medicine

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ObjectivesThis study was to evaluate the application of automatic measurement based on convolutional neural network (CNN) technology in intracavitary ultrasound cine of anterior pelvic.MethodsA total of 500 patients who underwent pelvic floor ultrasound examination at Peking University Shenzhen Hospital from July 2021 to February 2022 were retrospectively retrieved by the picture archiving and communication system (PACS) system, and 300 cases were used as a training set. The training set was labeled by three experienced ultrasound physicians to train CNN models and develop an automatic measurement software. The remaining 200 cases were used as a test set. Automatic measurement software identified relevant anatomical structures frame by frame and determined the two frames with the greatest difference, calculated the bladder neck descent (BND), urethral rotation angle (URA), and retrovesical angle (RA). Meanwhile, two experienced ultrasound physicians evaluated the resting frame and the maximum Valsalva frame on the cines by manual visual evaluation, labeled the anatomical structures in the corresponding frame, such as the inferoposterior margin of pubic symphysis, the mid‐axis of pubic symphysis, bladder contour, and urethra in the front, and calculated BND, URA, and RA. Considering that the residual urine volume (RUV) in the bladder may affect the results, enrolled patients were grouped according to the RUV (10–50 mL, 50–100 mL, and >100 mL). The consistency of the results by automatic measurement and manual visual evaluation was evaluated using the intraclass correlation coefficient (ICC) and the Bland–Altman graph.ResultsOf the 200 cases in the test set, 120 cases were successfully identified by the CNN automatic software with a 60% recognition rate. In the case of successful identification, the ICC of manual visual evaluation measurement and automatic measurement was 0.936 (BND), 0.911 (URA), 0.756 (RA in rest), and 0.877 (RA at maximum Valsalva), respectively. In addition, the RUV had a negligible effect on the consistency. The Bland–Altman plot shows the proportion of samples outside the limit was below 5%.ConclusionsCNN‐based automatic measurement software exhibited high reliability in anterior pelvic measurement, which results in a significantly enhanced measurement efficiency.

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Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound

Cited by 86 publications

References 32 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

Are All Deep Learning Architectures Alike for Point‐of‐Care Ultrasound?: Evidence From a Cardiac Image Classification Model Suggests Otherwise

Study on the Consistency Between Automatic Measurement Based on Convolutional Neural Network Technology and Manual Visual Evaluation in Intracavitary Ultrasonic Cine of Anterior Pelvic

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