Evaluation of a deep learning‐based computer‐aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images

2020 ◽  
Vol 47 (9) ◽  
pp. 3952-3960 ◽  
Author(s):  
Chao Sun ◽  
Yukang Zhang ◽  
Qing Chang ◽  
Tianjiao Liu ◽  
Shaohang Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Thyroid Nodules ◽  
Computer Aided Diagnosis ◽  
Ultrasound Images ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

Computer-aided diagnosis system for breast ultrasound images using deep learning

2019 ◽  
Vol 64 (23) ◽  
pp. 235013 ◽  
Author(s):  
Hiroki Tanaka ◽  
Shih-Wei Chiu ◽  
Takanori Watanabe ◽  
Setsuko Kaoku ◽  
Takuhiro Yamaguchi
Keyword(s):  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Computer-aided diagnosis system for breast ultrasound images using deep learning

2019 ◽  
Vol 45 ◽  
pp. S4 ◽  
Author(s):  
Hiroki Tanaka ◽  
Shih-Wei Chiu ◽  
Takanori Watanabe ◽  
Setsuko Kaoku ◽  
Takuhiro Yamaguchi
Keyword(s):  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals A Novel Interpretable Computer-Aided Diagnosis System of Thyroid Nodules on Ultrasound Based on Clinical Experience

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 53223-53231 ◽  
Author(s):  
Shijie Zhang ◽  
Huarui Du ◽  
Zhuang Jin ◽  
Yaqiong Zhu ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Clinical Experience ◽  
Thyroid Nodules ◽  
Computer Aided Diagnosis ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals A Deep Learning-based Computer-aided Diagnosis System for Mammographic Lesion Detection

2018 ◽  
Vol 54 (8) ◽  
pp. 659-669 ◽  
Author(s):  
Shintaro SUZUKI ◽  
Xiaoyong ZHANG ◽  
Noriyasu HOMMA ◽  
Kei ICHIJI ◽  
Yumi TAKANE ◽  
...  
Keyword(s):  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Lesion Detection ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

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

2019 ◽  
Vol 9 (4) ◽  
pp. 186-193
Author(s):  
Lei Xu ◽  
Junling Gao ◽  
Quan Wang ◽  
Jichao Yin ◽  
Pengfei Yu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Deep Learning ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Computer Aided Diagnosis ◽  
Cad Systems ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

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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