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

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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The Role and Impact of Deep Learning Methods in Computer-Aided Diagnosis Using Gastrointestinal Endoscopy

Diagnostics ◽

10.3390/diagnostics11040694 ◽

2021 ◽

Vol 11 (4) ◽

pp. 694

Author(s):

Xuejiao Pang ◽

Zijian Zhao ◽

Ying Weng

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Clinical Practice ◽

Gastrointestinal Endoscopy ◽

Computer Aided Diagnosis ◽

Learning Methods ◽

Learning Abilities ◽

Cad System ◽

Computer Aided ◽

Aided Diagnosis

At present, the application of artificial intelligence (AI) based on deep learning in the medical field has become more extensive and suitable for clinical practice compared with traditional machine learning. The application of traditional machine learning approaches to clinical practice is very challenging because medical data are usually uncharacteristic. However, deep learning methods with self-learning abilities can effectively make use of excellent computing abilities to learn intricate and abstract features. Thus, they are promising for the classification and detection of lesions through gastrointestinal endoscopy using a computer-aided diagnosis (CAD) system based on deep learning. This study aimed to address the research development of a CAD system based on deep learning in order to assist doctors in classifying and detecting lesions in the stomach, intestines, and esophagus. It also summarized the limitations of the current methods and finally presented a prospect for future research.

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The Diagnostic Efficiency of Ultrasound Computer–Aided Diagnosis in Differentiating Thyroid Nodules: A Systematic Review and Narrative Synthesis

Cancers ◽

10.3390/cancers11111759 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1759 ◽

Cited By ~ 2

Author(s):

Nonhlanhla Chambara ◽

Michael Ying

Keyword(s):

Systematic Review ◽

Diagnostic Accuracy ◽

Diagnostic Performance ◽

Thyroid Nodules ◽

Performance Outcomes ◽

Computer Aided Diagnosis ◽

Current Evidence ◽

Grey Scale ◽

Computer Aided ◽

Aided Diagnosis

Computer-aided diagnosis (CAD) techniques have emerged to complement qualitative assessment in the diagnosis of benign and malignant thyroid nodules. The aim of this review was to summarize the current evidence on the diagnostic performance of various ultrasound CAD in characterizing thyroid nodules. PUBMED, EMBASE and Cochrane databases were searched for studies published until August 2019. The Quality Assessment of Studies of Diagnostic Accuracy included in Systematic Review 2 (QUADAS-2) tool was used to assess the methodological quality of the studies. Reported diagnostic performance data were analyzed and discussed. Fourteen studies with 2232 patients and 2675 thyroid nodules met the inclusion criteria. The study quality based on QUADAS-2 assessment was moderate. At best performance, grey scale CAD had a sensitivity of 96.7% while Doppler CAD was 90%. Combined techniques of qualitative grey scale features and Doppler CAD assessment resulted in overall increased sensitivity (92%) and optimal specificity (85.1%). The experience of the CAD user, nodule size and the thyroid malignancy risk stratification system used for interpretation were the main potential factors affecting diagnostic performance outcomes. The diagnostic performance of CAD of thyroid ultrasound is comparable to that of qualitative visual assessment; however, combined techniques have the potential for better optimized diagnostic accuracy.

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A Beneficial Role of Computer-aided Diagnosis System for Less Experienced Physicians in the Diagnosis of Thyroid Nodule on Ultrasound

10.21203/rs.3.rs-568240/v1 ◽

2021 ◽

Author(s):

Sunyoung Kang ◽

Eunjung Lee ◽

Chae Won Chung ◽

Han Na Jang ◽

Joon Ho Moon ◽

...

Keyword(s):

Diagnostic Performance ◽

Thyroid Nodules ◽

Needle Aspiration ◽

Computer Aided Diagnosis ◽

Ultrasound Images ◽

Cad System ◽

Computer Aided ◽

Before And After ◽

Needle Aspiration Cytology ◽

Aided Diagnosis

Abstract Ultrasonography is the primary diagnostic tool for thyroid nodules, while the accuracy is highly operator-dependent. The aim of this study was to investigate whether ultrasonography with computer-aided diagnosis (CAD) has assisting roles to physicians in the diagnosis of thyroid nodules. 451 thyroid nodules (³ 1 cm) evaluated by fine-needle aspiration cytology following surgery were included. 300 (66.5%) of them were diagnosed as malignancy. Thirteen physicians who had 0 months (E0, n=8), 1 year (E1, n=2), or more than 5 years (E5, n=3) of experience in ultrasonography reviewed the prepared ultrasound images of thyroid nodules before and after CAD assistance. The diagnostic performance of CAD was comparable to that of the E5 group, and better than those of the E0 and E1 groups. The AUC of the CAD for conventional PTC was higher than that for FTC and follicular variant PTC (0.925 vs. 0.499), independent of tumor size. CAD assistance significantly improved diagnostic performance in E0 group, but not in the E1 and E5 groups. In conclusion, the CAD system showed good performance in the diagnosis of conventional PTC. CAD assistance improved the diagnostic performance of physicians with less experience in ultrasonography, especially in the diagnosis of conventional PTC.

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Diagnostic performance evaluation of different TI-RADS using ultrasound computer-aided diagnosis of thyroid nodules: An experience with adjusted settings

PLoS ONE ◽

10.1371/journal.pone.0245617 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0245617

Author(s):

Nonhlanhla Chambara ◽

Shirley Y. W. Liu ◽

Xina Lo ◽

Michael Ying

Keyword(s):

Diagnostic Performance ◽

Thyroid Nodules ◽

Computer Aided Diagnosis ◽

Ultrasound Images ◽

American Thyroid Association ◽

Korean Society ◽

Cad System ◽

Computer Aided ◽

Aided Diagnosis ◽

Default Setting

Background Thyroid cancer diagnosis has evolved to include computer-aided diagnosis (CAD) approaches to overcome the limitations of human ultrasound feature assessment. This study aimed to evaluate the diagnostic performance of a CAD system in thyroid nodule differentiation using varied settings. Methods Ultrasound images of 205 thyroid nodules from 198 patients were analysed in this retrospective study. AmCAD-UT software was used at default settings and 3 adjusted settings to diagnose the nodules. Six risk-stratification systems in the software were used to classify the thyroid nodules: The American Thyroid Association (ATA), American College of Radiology Thyroid Imaging, Reporting, and Data System (ACR-TIRADS), British Thyroid Association (BTA), European Union (EU-TIRADS), Kwak (2011) and the Korean Society of Thyroid Radiology (KSThR). The diagnostic performance of CAD was determined relative to the histopathology and/or cytology diagnosis of each nodule. Results At the default setting, EU-TIRADS yielded the highest sensitivity, 82.6% and lowest specificity, 42.1% while the ATA-TIRADS yielded the highest specificity, 66.4%. Kwak had the highest AUROC (0.74) which was comparable to that of ACR, ATA, and KSThR TIRADS (0.72, 0.73, and 0.70 respectively). At a hyperechoic foci setting of 3.5 with other settings at median values; ATA had the best-balanced sensitivity, specificity and good AUROC (70.4%; 67.3% and 0.71 respectively). Conclusion The default setting achieved the best diagnostic performance with all TIRADS and was best for maximizing the sensitivity of EU-TIRADS. Adjusting the settings by only reducing the sensitivity to echogenic foci may be most helpful for improving specificity with minimal change in sensitivity.

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A beneficial role of computer-aided diagnosis system for less experienced physicians in the diagnosis of thyroid nodule on ultrasound

Scientific Reports ◽

10.1038/s41598-021-99983-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Sunyoung Kang ◽

Eunjung Lee ◽

Chae Won Chung ◽

Han Na Jang ◽

Joon Ho Moon ◽

...

Keyword(s):

Diagnostic Performance ◽

Thyroid Nodules ◽

Needle Aspiration ◽

Computer Aided Diagnosis ◽

Cad System ◽

The Us ◽

Computer Aided ◽

Needle Aspiration Cytology ◽

Aided Diagnosis

AbstractUltrasonography (US) is the primary diagnostic tool for thyroid nodules, while the accuracy is operator-dependent. It is widely used not only by radiologists but also by physicians with different levels of experience. The aim of this study was to investigate whether US with computer-aided diagnosis (CAD) has assisting roles to physicians in the diagnosis of thyroid nodules. 451 thyroid nodules evaluated by fine-needle aspiration cytology following surgery were included. 300 (66.5%) of them were diagnosed as malignancy. Physicians with US experience less than 1 year (inexperienced, n = 10), or more than 5 years (experienced, n = 3) reviewed the US images of thyroid nodules with or without CAD assistance. The diagnostic performance of CAD was comparable to that of the experienced group, and better than those of the inexperienced group. The AUC of the CAD for conventional PTC was higher than that for FTC and follicular variant PTC (0.925 vs. 0.499), independent of tumor size. CAD assistance significantly improved diagnostic performance in the inexperienced group, but not in the experienced groups. In conclusion, the CAD system showed good performance in the diagnosis of conventional PTC. CAD assistance improved the diagnostic performance of less experienced physicians in US, especially in diagnosis of conventional PTC.

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Effect of a Deep Learning Framework-Based Computer-Aided Diagnosis System on the Diagnostic Performance of Radiologists in Differentiating between Malignant and Benign Masses on Breast Ultrasonography

Korean Journal of Radiology ◽

10.3348/kjr.2018.0530 ◽

2019 ◽

Vol 20 (5) ◽

pp. 749 ◽

Cited By ~ 16

Author(s):

Ji Soo Choi ◽

Boo-Kyung Han ◽

Eun Sook Ko ◽

Jung Min Bae ◽

Eun Young Ko ◽

...

Keyword(s):

Deep Learning ◽

Diagnostic Performance ◽

Computer Aided Diagnosis ◽

Breast Ultrasonography ◽

Diagnosis System ◽

Learning Framework ◽

Computer Aided ◽

Aided Diagnosis

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Computer-Aided Diagnosis for Capsule Endoscopy: From Inception to Future

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrted8094.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 12261-12273

Keyword(s):

Capsule Endoscopy ◽

Computer Aided Diagnosis ◽

Current Status ◽

Human Beings ◽

Abnormality Detection ◽

Gi Tract ◽

Cad Systems ◽

Cad System ◽

Computer Aided ◽

Aided Diagnosis

Background: Gastrointestinal (GI) tract abnormalities are most common across the world, and it is a significant threat to the health of human beings. Capsule endoscopy is a non-sedative, non-invasive and patient-friendly procedure for the diagnosis of GI tract abnormalities. However, it is very time consuming and tiresome task for physicians due to length of endoscopy videos. Thus computer-aided diagnosis (CAD) system is a must. Methods: This systematic review aims to investigate state-of-the-art CAD systems for automatic abnormality detection in capsule endoscopy by examining publications from scientific databases namely IEEE Xplore, Science Direct, Springer, and Scopus. Results: Based on defined search criteria and applied inclusion and exclusion criteria, 44 articles are included out of 187. This study presents the current status and analysis of CAD systems for capsule endoscopy. Conclusion: Publicly available larger dataset and a deep learning based CAD system may help to improve the efficiency of automated abnormality detection in capsule endoscopy.

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Development and Validation of a Deep Learning Algorithm to Automatic Detection of Pituitary Microadenoma From MRI

Frontiers in Medicine ◽

10.3389/fmed.2021.758690 ◽

2021 ◽

Vol 8 ◽

Author(s):

Qingling Li ◽

Yanhua Zhu ◽

Minglin Chen ◽

Ruomi Guo ◽

Qingyong Hu ◽

...

Keyword(s):

Deep Learning ◽

Diagnostic Accuracy ◽

Roc Curve ◽

Predictive Value ◽

Computer Aided Diagnosis ◽

Validation Dataset ◽

Cad System ◽

Pituitary Microadenoma ◽

Computer Aided ◽

Aided Diagnosis

Background: It is often difficult to diagnose pituitary microadenoma (PM) by MRI alone, due to its relatively small size, variable anatomical structure, complex clinical symptoms, and signs among individuals. We develop and validate a deep learning -based system to diagnose PM from MRI.Methods: A total of 11,935 infertility participants were initially recruited for this project. After applying the exclusion criteria, 1,520 participants (556 PM patients and 964 controls subjects) were included for further stratified into 3 non-overlapping cohorts. The data used for the training set were derived from a retrospective study, and in the validation dataset, prospective temporal and geographical validation set were adopted. A total of 780 participants were used for training, 195 participants for testing, and 545 participants were used to validate the diagnosis performance. The PM-computer-aided diagnosis (PM-CAD) system consists of two parts: pituitary region detection and PM diagnosis. The diagnosis performance of the PM-CAD system was measured using the receiver operating characteristics (ROC) curve and area under the ROC curve (AUC), calibration curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score.Results: Pituitary microadenoma-computer-aided diagnosis system showed 94.36% diagnostic accuracy and 98.13% AUC score in the testing dataset. We confirm the robustness and generalization of our PM-CAD system, the diagnostic accuracy in the internal dataset was 96.50% and in the external dataset was 92.26 and 92.36%, the AUC was 95.5, 94.7, and 93.7%, respectively. In human-computer competition, the diagnosis performance of our PM-CAD system was comparable to radiologists with >10 years of professional expertise (diagnosis accuracy of 94.0% vs. 95.0%, AUC of 95.6% vs. 95.0%). For the misdiagnosis cases from radiologists, our system showed a 100% accurate diagnosis. A browser-based software was designed to assist the PM diagnosis.Conclusions: This is the first report showing that the PM-CAD system is a viable tool for detecting PM. Our results suggest that the PM-CAD system is applicable to radiology departments, especially in primary health care institutions.

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A computer-aided diagnosis system for lung nodule detection in pulmonary CT images based on LS-SVM and wavelet transformation

10.32920/ryerson.14655537.v1 ◽

2021 ◽

Author(s):

Omid Talakoub

Keyword(s):

Wavelet Transformation ◽

Ct Images ◽

Computer Aided Diagnosis ◽

Regions Of Interest ◽

Support Vector ◽

Cad Systems ◽

Cad System ◽

Computer Aided ◽

Spherical Structures ◽

Aided Diagnosis

One of the most important areas of biomedical engineering is medical imaging. Fully automated schemes are currently being explored as Computer-Aided Diagnosis (CAD) systems to provide a second opinion to medical professionals; of these systems, abnormal region detector in medical images is one of the most critical CAD systems in development. The primary motivation in using these systems is due to the fact that reading an enormous number of images is a time-consuming task for the radiologist. This task can be sped up by using a CAD system which highlights abnormal regions of interest. Low false positive rates and high sensitivity are essential requirement[s] of such a system. The initial requirement of processing any organ is an accurate segmentation of the target of interest in the images. A segmentation method based on the wavelet transformation is proposed which accurately extracts lung regions in the thoracic CT images. After this step, an Aritifical Intelligence system, known as Least Squares Support Vector Machine (LS-SVM), is employed to classify nodules within the regions of interest. It is a well known fact that the lung nodules, except the pleural nodules, are mostly spherical structures whereas other structures including blood vessels are shaped as other structures such as tubular. Therfore, an enhancment filter is developed in which spherical structures are accentuated. Processing three different real databases revealed that the proposed system has reached the objective of a CAD system to provide reliable opinion for the doctors in the diagnosis fashion.

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