Computer Aided Diagnosis – A Review of Research

1978 ◽  
Vol 17 (01) ◽  
pp. 15-28 ◽  
Author(s):  
Ann Wardle ◽  
L. Wardle
Keyword(s):  
Diagnostic Accuracy ◽  
Statistical Models ◽  
Bayesian Models ◽  
Computer Aided Diagnosis ◽  
Medical Data ◽  
Thought Processes ◽  
Clinical Methods ◽  
Computer Aided ◽  
Probabilistic Nature ◽  
Aided Diagnosis

Computer aided diagnosis is reviewed and an assessment made of the different models used for this purpose. These are discussed under three headings:1. Models based on physicians’ thought processes2. Models based on the physiological relationships manifested in the disease state,3. Statistical models.The models are assessed in terms of their accuracy by comparison with clinical methods. Typical results show that using the computer improves diagnostic accuracy by about 10%.It is concluded that Bayesian models are likely to give the best results since they are well suited to the probabilistic nature of medical data and can be adapted for use in many situations. Further research necessary for the improvement of Bayesian models is discussed.

scholarly journals Deep Learning-Based Computer-Aided Diagnosis System for Gastroscopy Image Classification Using Synthetic Data

2021 ◽  
Vol 11 (2) ◽  
pp. 760
Author(s):  
Yun-ji Kim ◽  
Hyun Chin Cho ◽  
Hyun-chong Cho
Keyword(s):  
Deep Learning ◽  
Disease Diagnosis ◽  
Computer Aided Diagnosis ◽  
Medical Data ◽  
Classification Model ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Original Training ◽  
Computer Aided ◽  
Aided Diagnosis

Gastric cancer has a high mortality rate worldwide, but it can be prevented with early detection through regular gastroscopy. Herein, we propose a deep learning-based computer-aided diagnosis (CADx) system applying data augmentation to help doctors classify gastroscopy images as normal or abnormal. To improve the performance of deep learning, a large amount of training data are required. However, the collection of medical data, owing to their nature, is highly expensive and time consuming. Therefore, data were generated through deep convolutional generative adversarial networks (DCGAN), and 25 augmentation policies optimized for the CIFAR-10 dataset were implemented through AutoAugment to augment the data. Accordingly, a gastroscopy image was augmented, only high-quality images were selected through an image quality-measurement method, and gastroscopy images were classified as normal or abnormal through the Xception network. We compared the performances of the original training dataset, which did not improve, the dataset generated through the DCGAN, the dataset augmented through the augmentation policies of CIFAR-10, and the dataset combining the two methods. The dataset combining the two methods delivered the best performance in terms of accuracy (0.851) and achieved an improvement of 0.06 over the original training dataset. We confirmed that augmenting data through the DCGAN and CIFAR-10 augmentation policies is most suitable for the classification model for normal and abnormal gastric endoscopy images. The proposed method not only solves the medical-data problem but also improves the accuracy of gastric disease diagnosis.

Diagnostic Accuracy and Reading Time to Detect Intracranial Aneurysms on MR Angiography Using a Computer-Aided Diagnosis System

2008 ◽  
Vol 190 (2) ◽  
pp. 459-465 ◽  
Author(s):  
Shingo Kakeda ◽  
Yukunori Korogi ◽  
Hidetaka Arimura ◽  
Toshinori Hirai ◽  
Shigehiko Katsuragawa ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Intracranial Aneurysms ◽  
Mr Angiography ◽  
Reading Time ◽  
Computer Aided Diagnosis ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

Relative gains in diagnostic accuracy between computer-aided diagnosis and independent double reading

2000 ◽  
Author(s):  
Yulei Jiang ◽  
Robert M. Nishikawa ◽  
Robert A. Schmidt ◽  
Charles E. Metz ◽  
Kunio Doi
Keyword(s):  
Diagnostic Accuracy ◽  
Computer Aided Diagnosis ◽  
Double Reading ◽  
Computer Aided ◽  
Relative Gains ◽  
Aided Diagnosis

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.

A Method for Estimating Prognosis Based on Physician’s Decision-Making Process

1981 ◽  
Vol 20 (04) ◽  
pp. 202-206 ◽  
Author(s):  
Ch. P. Peev ◽  
S. Kaihara
Keyword(s):  
Decision Making ◽  
Statistical Models ◽  
Cerebrovascular Diseases ◽  
Medical Data ◽  
The Other ◽  
Decision Making Process ◽  
Empirical Risk ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Measure Error

Different diagnostic rules for computer-aided diagnosis are based on different mathematically precise statistical models. In practice, however, the medical data cannot meet the requirements set for the models and, in some cases, the model precision loses its advantages. On the other hand, physicians make their decisions without mathematical precision according to some statistics based on their own experiences.In this study, the physician’s process of estimating prognosis of diseases was analyzed and a method for estimating prognosis based on the physician’s decision-making process was proposed. Problems such as collection of informative symptoms, their estimation and weighting, and physician’s decision were considered. The decisionmaking function obtained from the analysis was applied for estimating the prognosis of cerebrovascular diseases. The choice of informative symptoms was based on Kullback’s information measure. Error estimation was made by using the minimum empirical risk method. The proposed method seemed to provide a smaller error rate, as compared to discriminant analysis under identical conditions (same sample, same informative symptoms).

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

Cancers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1759 ◽  
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.

Optical diagnosis of colorectal polyp images using a newly developed computer-aided diagnosis system (CADx) compared to intuitive optical diagnosis

Endoscopy ◽  
2020 ◽  
Author(s):  
Quirine E.W. van der Zander ◽  
Ramon Michel Schreuder ◽  
Roger Fonollà ◽  
Thom Scheeve ◽  
Fons van der Sommen ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Multimodal Imaging ◽  
Narrow Band Imaging ◽  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Optical Diagnosis ◽  
High Definition ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Optical diagnosis of colorectal polyps (CRPs) remains challenging. Imaging enhancement techniques such as narrow band imaging and blue light imaging (BLI) can improve optical diagnosis. We developed and prospectively validated a computer-aided diagnosis system (CADx) using high definition white light (HDWL) and BLI images, and compared it with the optical diagnosis of expert and novice endoscopists. Methods: The CADx characterized CRPs by exploiting artificial neural networks. Six experts and thirteen novices optically diagnosed 60 CRPs based on intuition. After a washout period of four weeks, the same set of CRPs was permuted and optically diagnosed using BASIC (BLI Adenoma Serrated International Classification). Results: The CADx had a diagnostic accuracy of 88.3% using HDWL images and 86.7% using BLI images. The overall diagnostic accuracy, combining HDWL and BLI (multimodal imaging), was 95.0% and significantly higher compared to experts (81.7%, p=0.031) and novices (66.5%, p<0.001). Sensitivity (95.6% vs. 61.1% and 55.4%) was also higher for CADx, while specificity was higher for experts compared to CADx and novices (94.1% vs 93.3% and 92.1%). For endoscopists, diagnostic accuracy did not increase using BASIC, neither for experts (Intuition 79.5% vs BASIC 81.7%, p=0.140) nor for novices (Intuition 66.7% vs BASIC 66.5%, p=0.953). Conclusion: The CADx had a significantly higher diagnostic accuracy than experts and novices for the optical diagnosis of CRPs. Multimodal imaging, incorporating both HDWL and BLI, improved the diagnostic accuracy of the CADx. BASIC did not increase the diagnostic accuracy of endoscopists compared to intuitive optical diagnosis.

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