Ultrasound image analysis using deep learning algorithm for the diagnosis of thyroid nodules

Song, Junho; Chai, Young Jun; Masuoka, Hiroo; Park, Sun Won; Kim, Su Jin; Choi, June Young; Kong, Hyoun‐Joong; Lee, Kyu Eun; Lee, Joongseek; Kwak, Nojun; Yi, Ka Hee; Miyauchi, Akira

doi:10.1097/md.0000000000015133

Cited by 74 publications

(61 citation statements)

References 36 publications

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“…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: Methodological Quality Of the Included Studiesmentioning

confidence: 99%

Section: Literature Searches and Description Of Studiesmentioning

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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“…AI has also been successfully applied to thyroid ultrasound. In a study by Song et al, 1358 thyroid nodule US images were used to train a DL algorithm . The objective of the study was to determine if AI could differentiate malignant from benign thyroid nodules, thus reducing the number of thyroid biopsies performed.…”

Section: Artificial Intelligencementioning

confidence: 99%

Emerging technologies in neuromuscular ultrasound

Hobson‐Webb

2020

Muscle and Nerve

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Neuromuscular ultrasound is an accepted and valuable element in the evaluation of peripheral nerve and muscle disease. However, ultrasound has several limitations to consider, including operator dependency and lack of a viable contrast agent. Fortunately, new technological advances show promise in resolving these issues. Ultrahigh resolution ultrasound enables imaging of the nerve at the fascicular level. Shear wave elastography imaging can provide measures of tissue stiffness that can act as a surrogate measure of nerve and muscle health. Photoacoustic imaging may overcome neuromuscular ultrasound's current lack of contrast agents to detect inflammation and other functional changes within nerve and muscle, while artificial intelligence stands to address operator dependency and improve diagnostic imaging. The basic principles of each of these technologies are discussed along with current research and potential future applications in neuromuscular imaging. K E Y W O R D S artificial intelligence, elastography, high resolution ultrasound, neuromuscular ultrasound, photoacoustic imaging

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“…This kind of system uses and processes one or more captured medical images of some organs such as X-ray, CT, and MRI scans, and yields its decision that can assist doctors in diagnosing diseases. Due to its purpose, CAD systems have been widely developed and used in real-life applications such as for diagnosing the brain [2,3,7,12], breasts [4,8,[13][14][15][16], lungs [10], and thyroid diseases [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence

Nguyen

Kang

Pham

et al. 2020

Sensors

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Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such systems plays an important role in enhancing the quality of a diagnosing task. Although there have been the state-of-the art studies regarding this problem, which are based on handcrafted features, deep features, or the combination of the two, their performances are still limited. To overcome these problems, we propose an ultrasound image-based diagnosis of the malignant thyroid nodule method using artificial intelligence based on the analysis in both spatial and frequency domains. Additionally, we propose the use of weighted binary cross-entropy loss function for the training of deep convolutional neural networks to reduce the effects of unbalanced training samples of the target classes in the training data. Through our experiments with a popular open dataset, namely the thyroid digital image database (TDID), we confirm the superiority of our method compared to the state-of-the-art methods.

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Ultrasound image analysis using deep learning algorithm for the diagnosis of thyroid nodules

Abstract: Supplemental Digital Content is available in the text

Cited by 74 publications

References 36 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

Emerging technologies in neuromuscular ultrasound

Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence

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