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Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System
摘要： Purpose: To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial intelligence (AI) system. Materials and Methods: An enriched retrospective, fully crossed, multireader, multicase, HIPAA-compliant study was performed. Screening digital mammographic examinations from 240 women (median age, 62 years; range, 39–89 years) performed between 2013 and 2017 were included. The 240 examinations (100 showing cancers, 40 leading to false-positive recalls, 100 normal) were interpreted by 14 Mammography Quality Standards Act–qualified radiologists, once with and once without AI support. The readers provided a Breast Imaging Reporting and Data System score and probability of malignancy. AI support provided radiologists with interactive decision support (clicking on a breast region yields a local cancer likelihood score), traditional lesion markers for computer-detected abnormalities, and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), specificity and sensitivity, and reading time were compared between conditions by using mixed-models analysis of variance and generalized linear models for multiple repeated measurements. Results: On average, the AUC was higher with AI support than with unaided reading (0.89 vs 0.87, respectively; P = .002). Sensitivity increased with AI support (86% [86 of 100] vs 83% [83 of 100]; P = .046), whereas specificity trended toward improvement (79% [111 of 140]) vs 77% [108 of 140]; P = .06). Reading time per case was similar (unaided, 146 seconds; supported by AI, 149 seconds; P = .15). The AUC with the AI system alone was similar to the average AUC of the radiologists (0.89 vs 0.87). Conclusion: Radiologists improved their cancer detection at mammography when using an artificial intelligence system for support, without requiring additional reading time.
关键词： mammography,computer-aided detection,breast cancer,deep learning,artificial intelligence
更新于2025-09-23 15:21:01
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New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence
摘要： OBJECTIVE. The purpose of this article is to compare traditional versus machine learning–based computer-aided detection (CAD) platforms in breast imaging with a focus on mammography, to underscore limitations of traditional CAD, and to highlight potential solutions in new CAD systems under development for the future. CONCLUSION. CAD development for breast imaging is undergoing a paradigm shift based on vast improvement of computing power and rapid emergence of advanced deep learning algorithms, heralding new systems that may hold real potential to improve clinical care.
关键词： computer-aided detection,breast,artificial intelligence,mammography,texture analysis,computer-aided diagnosis
更新于2025-09-19 17:15:36
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Automatic computer-aided analysis of optic disc pallor in fundus photographs
摘要： Purpose: Assessment of optic disc pallor in fundus photographs may be frequently misinterpreted due to the subjective nature of interpretation. We developed a fully automatic computer-aided detection (CAD) system for optic disc pallor using colour fundus photographs and evaluated the accuracy of the system. Methods: A newly proposed CAD system was developed for automated segmentation and image analysis of optic disc pallor, and a logistic regression model was developed for risk analysis. A total of 230 photographs with variable degree of optic disc pallor, and 123 normal optic discs confirmed by optical coherence tomography were tested for validation of the software. Sensitivity and specificity of the CAD system in automatic detection of optic disc pallor using colour fundus photographs were evaluated. The results of manual detection of optic disc pallor on fundus photographs by two independent ophthalmologists were compared with the efficacy of the CAD system. Results: The fully automated CAD system achieved a sensitivity of 95.3% and a specificity of 96.7% for detecting optic disc pallor in colour fundus images. The overall accuracy of the CAD system was 96.1%, which was superior to the results of manual detection by individual examiners. Conclusions: We developed an automated CAD system that successfully detected optic disc pallor in fundus photographs. The proposed algorithm can assist the clinical judgement of ophthalmologists for detecting optic disc pallor in fundus photographs.
关键词： automatic,computer-aided detection,optic disc,pallor
更新于2025-09-11 14:15:04
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Artificial Intelligence in Breast Imaging: Potentials and Limitations
摘要： The purpose of this article is to discuss potential applications of artificial intelligence (AI) in breast imaging and limitations that may slow or prevent its adoption. The algorithms of AI for workflow improvement and outcome analyses are advancing. Using imaging data of high quality and quantity, AI can support breast imagers in diagnosis and patient management, but AI cannot yet be relied on or be responsible for physicians’ decisions that may affect survival. Education in AI is urgently needed for physicians.
关键词： computer-aided detection and diagnosis,machine and deep learning,artificial intelligence in breast imaging,artificial intelligence in radiology,artificial neural networks
更新于2025-09-10 09:29:36
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Weakly-Supervised Lesion Detection from Fundus Images
摘要： Early diagnosis and continuous monitoring of patients suffering from eye diseases have been major concerns in the computer-aided detection (CAD) techniques. Detecting one or several specific types of retinal lesions has made a significant breakthrough in computed-aid screen in the past few decades. However, due to variety of retinal lesions and complex normal anatomical structures, automatic detection of lesions with unknown and diverse types from a retina remains a challenging task. In this paper, a weakly supervised method, requiring only a series of normal and abnormal retinal images without need to specifically annotate their locations and types, is proposed for this task. Specifically, a fundus image is understood as a superposition of background, blood vessels and background noise (lesions included for abnormal images). Background is formulated as low-rank structure after a series of simple preprocessing steps, including spatial alignment, color normalization and blood vessels removal. Background noise is regarded as stochastic variable and modeled through Gaussian for normal images and mixture of Gaussian (MoG) for abnormal images, respectively. The proposed method encodes both the background knowledge of fundus images and the background noise into one unique model, and corporately optimize the model using normal and abnormal images, which fully depict the low-rank subspace of the background and distinguish the lesions from background noise in abnormal fundus images. Experimental results demonstrate that the proposed method is of fine arts accuracy and outperforms the previous related methods.
关键词： mixture of Gaussian,computer-aided detection,low-rank structure,weakly supervised learning,retinal lesions
更新于2025-09-09 09:28:46