Abstract
Objective: This study evaluates the effectiveness of artificial intelligence (AI) in mammography in a diverse population from a middle-income nation and compares it to traditional methods.
Methods: A retrospective study was conducted on 543 mammograms of 467 Malays, 48 Chinese, and 28 Indians in a middle-income nation. Three breast radiologists interpreted the examinations independently in two reading sessions (with and without AI support). Breast density and BI-RADS categories were assessed, comparing the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) results.
Results: Of 543 mammograms, 69.2% had lesions detected. Biopsies were performed on 25%(n=136), with 66(48.5%) benign and 70(51.5%) malignant. Substantial agreement in density assessment between the radiologist and AI software (κ =0.606, p < 0.001) and the BI-RADS category with and without AI (κ =0.74, p < 0.001). The performance of the AI software was comparable to the traditional methods. The sensitivity, specificity, PPV, and NPV or radiologists alone, radiologist + AI, and AI alone were 81.9%,90.4%,56.0%, and 97.1%; 81.0%, 93.1%,55.5%, and 97.0%; and 90.0%,76.5%,36.2%, and 98.1%, respectively. AI software enhances the accuracy of lesion diagnosis and reduces unnecessary biopsies, particularly for BI-RADS 4 lesions. The AI software results for synthetic were almost similar to the original 2D mammography, with AUC of 0.925 and 0.871, respectively.
Conclusion: AI software may assist in the accurate diagnosis of breast lesions, enhancing the efficiency of breast lesion diagnosis in a mixed population of opportunistic screening and diagnostic patients.
Key Messages:
• The use of artificial intelligence (AI) in mammography for population-based breast cancer screening has been validated in high-income nations, with reported improved diagnostic performance. Our study evaluated the usage of an AI tool in an opportunistic screening setting in a multi-ethnic and middle-income nation.
• The application of AI in mammography enhances diagnostic accuracy, potentially leading to reduced unnecessary biopsies.
• AI integration into the workflow did not disrupt the performance of trained breast radiologists, as there is a substantial inter-reader agreement for BI-RADS category assessment and breast density.
Keywords: Artificial intelligence, Breast cancer, C-view, Density, Mammography, Multi-ethnic, Screening.
Call for Papers in Thematic Issues
Deep Learning Approaches for Image Enhancement in Medical Diagnostics
The medical and healthcare sectors are very different from one another. Consumers in these sectors need more affordable, superior offerings. Health or medical specialists are typically the ones who analyse medical data. In particular, medical imaging evaluation heavily relies on machine learning and deep learning. They not only yield outstanding ...read more
Emerging Biomedical Imaging Informatic for Smart Healthcare
Biomedical imaging informatics is a field that deals with the acquisition, processing, and interpretation of images in biomedicine using computational approaches. Biomedical imaging has progressed from early, simple X-rays for fracture diagnosis and foreign body identification to various techniques used to research biological structure and function and answer fundamental biomedical ...read more
Emerging Trends & Techniques in Medical Imaging using Machine Learning and Deep Learning Techniques
This special issue will aim to discuss different approaches, initiatives and applications in Medical Imaging fields (including Machine Intelligence, Mining Engineering, Modeling and Simulation, Computer, Communication, Networking and Information Engineering, Systems Engineering, Innovative Computing Systems, Adaptive Technologies for Sustainable Growth, and Theoretical and Applied Sciences). This collection should inspire various ...read more
Multimodal Imaging Using Spectroscopic Techniques for Improved Surgical Guidance
The majority of patients with solid malignancies have surgical excision as their primary and most successful method of medical care. On the other hand, patients experience recurrence and subsequent recurrence. Their objective was to provide a simpler and more concise explanation of the obstacles faced by physicians and the ways ...read more
17