Hamid Marlina Tanty Ramli, Mumin Nazimah Ab, Hamid Shamsiah Abdul, Rahmat Kartini
Department of Radiology, Faculty of Medicine University Teknologi MARA, Sungai Buloh, Selangor, Malaysia.
Department of Biomedical Imaging, University Malaya Research Imaging Centre, Kuala Lumpur, Malaysia.
Curr Med Imaging. 2024 Feb 27. doi: 10.2174/0115734056280191231207052903.
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.
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.
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.
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.
• 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.
本研究评估人工智能（AI）在中等收入国家不同人群乳腺钼靶检查中的有效性，并将其与传统方法进行比较。
对来自中等收入国家的467名马来人、48名中国人和28名印度人的543份乳腺钼靶图像进行回顾性研究。三名乳腺放射科医生在两个阅读环节（有和没有AI支持）中独立解读检查结果。评估乳腺密度和BI-RADS分类，比较准确性、敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）结果。
在543份乳腺钼靶图像中，69.2%检测到病变。25%（n = 136）的患者进行了活检，其中66例（48.5%）为良性，70例（51.5%）为恶性。放射科医生与AI软件在密度评估方面有高度一致性（κ = 0.606，p < 0.001），在有和没有AI的情况下BI-RADS分类也有高度一致性（κ = 0.74，p < 0.001）。AI软件的性能与传统方法相当。单独由放射科医生、放射科医生 + AI以及单独由AI进行诊断时，敏感性、特异性、PPV和NPV分别为81.9%、90.4%、56.0%和97.1%；81.0%、93.1%、55.5%和97.0%；以及90.0%、76.5%、36.2%和98.1%。AI软件提高了病变诊断的准确性，减少了不必要的活检，特别是对于BI-RADS 4类病变。合成图像的AI软件结果与原始二维乳腺钼靶图像几乎相似，AUC分别为0.925和0.871。
AI软件可能有助于乳腺病变的准确诊断，提高机会性筛查和诊断患者混合人群中乳腺病变诊断的效率。
• 在高收入国家，人工智能（AI）在基于人群的乳腺癌筛查乳腺钼靶检查中的应用已得到验证，报告显示诊断性能有所提高。我们的研究评估了AI工具在多民族中等收入国家机会性筛查环境中的使用情况。
• AI在乳腺钼靶检查中的应用提高了诊断准确性，可能减少不必要的活检。
• 将AI整合到工作流程中并未干扰训练有素的乳腺放射科医生的表现，因为在BI-RADS分类评估和乳腺密度方面读者之间有高度一致性。
