Fusion US/MRI prostate biopsy using a computer aided diagnostic (CAD) system

Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this study is to assess the diagnostic performance of non-experienced readers when reporting assisted by the likelihood map generated by a CAD system, and to compare the results with the unassisted interpretation. Three resident radiologists were asked to review multiparametric-MRI of patients with and without PCa, both unassisted and assisted by a CAD system. In both reading sessions, residents recorded all positive cases, and sensitivity, specificity, negative and positive predictive values were computed and compared. The dataset comprised 90 patients (45 with at least one clinically significant biopsy-confirmed PCa). Sensitivity significantly increased in the CAD assisted mode for patients with at least one clinically significant lesion (GS > 6) (68.7% vs. 78.1%, p = 0.018). Overall specificity was not statistically different between unassisted and assisted sessions (94.8% vs. 89.6, p = 0.072). The use of the CAD system significantly increases the per-patient sensitivity of inexperienced readers in the detection of clinically significant PCa, without negatively affecting specificity, while significantly reducing overall reporting time.

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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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CATI: A Computer Aided Tolerancing Interface

18th Design Automation Conference: Volume 2 — Geometric Modeling, Mechanisms, and Mechanical Systems Analysis ◽

10.1115/detc1992-0161 ◽

1992 ◽

Author(s):

Rikard Söderberg

Keyword(s):

Normal Distribution ◽

Tolerance Analysis ◽

Distribution Model ◽

Preliminary Design ◽

Tolerance Limits ◽

Cad System ◽

System A ◽

Computer Aided ◽

2D Drawing ◽

Normal Distribution Model

Abstract This work presents an interface for tolerance analysis in a CAD system. A method for picking up necessary information from a 2D drawing is developed and implemented as an interface in a commercial CAD system. The interface communicates with an external calculation program which determines unknown tolerance limits using the normal distribution model. Results from the calculation program is in the end used by the interface to present measures with tolerances on the drawing. The advantage of using CATI in preliminary design is discussed, and a strategy for treating interrelated tolerance chains is presented.

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Integration of Commercial CAD/CAM System With Custom CAPP Using Orbix Middleware and CORBA Standard

Volume 2: 28th Design Automation Conference ◽

10.1115/detc2002/dac-34069 ◽

2002 ◽

Author(s):

Fernando Rangel ◽

Jami J. Shah

Keyword(s):

Computer Aided Design ◽

Numerical Control ◽

Commercial Application ◽

Commercial Software ◽

Computer Aided Process Planning ◽

Nc Code ◽

Cad System ◽

Computer Aided ◽

Continuous Application ◽

Part Model

This paper discusses the issues of integrating the Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) programs in commercial software. Integration was achieved through implementation of a computer-aided process planning (CAPP) system within the commercial software. The part model was imported into, or designed in, the commercial CAD system. Manufacturing information was then extracted from the part model by the CAPP system using commercial Application Programming Interfacing (API) methods. The CAPP system then uses the extracted information to produce a process plan consistent with the requirements of the commercial CAM module to produce Numerical Control (NC) code. The internal integration was accomplished using commercial API methods that dynamically bind the CAD, CAPP, and CAM into a single continuous application. These APIs are implemented using the Orbix middleware following the CORBA standard. A case study demonstrating the integration is presented. Strengths and weaknesses of integrating the CAD and CAM domains using APIs and middleware are discussed.

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Sensitivity of Computer-Aided Detection (CAD) Device for Lung Nodule Detection on Chest Radiography: A Real-Life Application

Radiology and Medical Diagnostic Imaging ◽

10.31487/j.rdi.2020.02.05 ◽

2020 ◽

pp. 1-5

Author(s):

Ammar Chaudhry ◽

William H. Moore

Keyword(s):

Sensitivity And Specificity ◽

Chest Radiography ◽

Predictive Value ◽

Lung Nodule ◽

Chest Radiographs ◽

Lung Nodules ◽

Computer Aided Detection ◽

Cad System ◽

Computer Aided ◽

The Mean

Purpose: The radiographic diagnosis of lung nodules is associated with low sensitivity and specificity. Computer-aided detection (CAD) system has been shown to have higher accuracy in the detection of lung nodules. The purpose of this study is to assess the effect on sensitivity and specificity when a CAD system is used to review chest radiographs in real-time setting. Methods: Sixty-three patients, including 24 controls, who had chest radiographs and CT within three months were included in this study. Three radiologists were presented chest radiographs without CAD and were asked to mark all lung nodules. Then the radiologists were allowed to see the CAD region-of-interest (ROI) marks and were asked to agree or disagree with the marks. All marks were correlated with CT studies. Results: The mean sensitivity of the three radiologists without CAD was 16.1%, which showed a statistically significant improvement to 22.5% with CAD. The mean specificity of the three radiologists was 52.5% without CAD and decreased to 48.1% with CAD. There was no significant change in the positive predictive value or negative predictive value. Conclusion: The addition of a CAD system to chest radiography interpretation statistically improves the detection of lung nodules without affecting its specificity. Thus suggesting CAD would improve overall detection of lung nodules.

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CAFP: Computer-Aided Fixture Planning

13th Computers in Engineering Conference ◽

10.1115/cie1993-0078 ◽

1993 ◽

Author(s):

Jerry Y. Fuh ◽

Chao-Hwa Chang ◽

Michel A. Melkanoff ◽

Hsin Rau

Keyword(s):

Process Planning ◽

Rational Approach ◽

Machining Processes ◽

Constraint Equations ◽

Cad System ◽

Manufacturing Process Planning ◽

Modular Fixtures ◽

Fixture Planning ◽

Computer Aided ◽

The Common

Abstract Fixture planning is an indispensable part of a manufacturing process planning routine. This paper introduces a rational approach to computer-aided fixture planning (CAFP). A method of fixture classification and selection is introduced for planning of modular fixtures. The location of each fixture component is determined according to the common fixturing principles. We has developed a prototype CAFP system and linked to a commercial CAD system, namely CADAM. Modular fixture elements are automatically selected by the system, and the generated fixture layout can be displayed on-screen after assembly. For verification and optimization of a fixturing scheme, a fixture analysis module is also developed. An iteration method is used to solve the fixturing constraint equations and to determine the adequate clamping forces for holding the workpiece during machining processes.

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Abstract T P32: Computer Aided Detection of Asymptomatic Cerebral Microbleeds on Magnetic Resonance Images

Stroke ◽

10.1161/str.45.suppl_1.tp32 ◽

2014 ◽

Vol 45 (suppl_1) ◽

Author(s):

Yan Li ◽

Yining Huang ◽

Jue Zhang ◽

Jing Fang

Keyword(s):

Magnetic Resonance ◽

Rating Scale ◽

Characteristic Curve ◽

Magnetic Resonance Images ◽

False Positives ◽

Cerebral Microbleeds ◽

Cad System ◽

Automated Method ◽

Skull Stripping ◽

Computer Aided

Purpose: Manual rating of Cerebral microbleeds (CMBs) is time-consuming and inconsistent. Since the presence and number of CMBs have become a potential diagnostic and prognostic biomarker of stroke, an automatic identification method is required. We proposed a computer aided diagnosis (CAD) system for the detection of the CMBs on the magnetic resonance (MR) images automatically. Methods: Eighty-one patients were recruited in this study. CMBs on the MR T2* weighted images were manually rated according to the Microbleed Anatomic Rating Scale (MARS) criteria. Our automated method consisted of two steps: i) Pre-processing: After skull stripping, isolated islands of points were removed while holes were restored to avoid over segmentation. Local threshold segmentation was applied for the initial candidate selection. ii) Identification model: Seven features were extracted from each candidate: area, roundness, intensity, average of the boundary, contrast, shape-intensity and location-mark (according to the probability density templates calculated from the location information of the CMBs). For further identification of each candidate, Random Forest (RF) model was used to distinguish CMBs from the mimics. Results: A total of 337 CMBs in the 81 patients were studied. Comparing with the counting from the experienced doctors, high sensitivity of 92% (310/337) was achieved after pre-processing. The RF model eliminated most of the false-positives while maintaining a reliable sensitivity of 94% (291/310) and specificity of 96% (4272/4450). The area under the Receiver operating characteristic curve was 0.98 ± 0.02 for the detection model. In summary, this CAD system had an overall sensitivity of 86% (291/337) and specificity of 96% (4272/4450), producing only 2.2 false-positives per subject. Conclusion: This presented strategy is technically effective. The results indicate that it has the potential to be used for clinical detection of CMBs.

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