Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography

2020 ◽  
Vol 38 (11) ◽  
pp. 1052-1061 ◽  
Author(s):  
Takenori Kozuka ◽  
Yuko Matsukubo ◽  
Tomoya Kadoba ◽  
Teruyoshi Oda ◽  
Ayako Suzuki ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Pulmonary Nodules ◽  
Computer Aided Diagnosis ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

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

Diagnostics ◽  
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.

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

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.

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.

scholarly journals Survey on Recent Works in Computed Tomography based Computer ‑ Aided Diagnosis of Liver using Deep Learning Techniques

2020 ◽  
Vol 5 (7) ◽  
pp. 173-181
Author(s):  
E. Emerson Nithiyaraj ◽  
S. Arivazhagan
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Imaging Modality ◽  
Computer Aided Diagnosis ◽  
Lesion Detection ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
X Rays ◽  
Computer Aided ◽  
Aided Diagnosis

Computed tomography (CT) scanning is a non-invasive diagnostic imaging technique that provides more detailed information about the liver than standard X-rays. Unlike ultrasound (US) examination, the quality of the CT image is not highly operator dependent. Plenty of works has been done using computer-aided diagnosis (CAD) for liver using conventional machine learning algorithms with better results. Recent advances especially in deep learning technology, can detect, classify, segment patterns in medical images where the advancements in deep learning has been shifted to medical domain also. One of the core abilities of deep learning is that they could learn feature representations automatically from data instead of feeding hand crafted features based on application. In this review, the basics of deep learning is introduced and their success in liver segmentation and lesion detection, classification using CT imaging modality is reviewed and their different network architectures is also discussed. Transfer learning is an interesting approach in deep learning which is also discussed. So, deep learning and CAD system has made a huge impact and has produced enhanced performance in healthcare industry.

scholarly journals A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning

2019 ◽  
Vol 51 ◽  
pp. 101-115 ◽  
Author(s):  
Naji Khosravan ◽  
Haydar Celik ◽  
Baris Turkbey ◽  
Elizabeth C. Jones ◽  
Bradford Wood ◽  
...  
Keyword(s):  
Deep Learning ◽  
Eye Tracking ◽  
Computer Aided Diagnosis ◽  
Attentional Model ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis
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