人工智能(AI)越来越多地用于医学,以提高疾病诊断和治疗的速度和准确性。基于AI的图像分析有望在未来的医疗机构和实验室中发挥关键作用。提供更高的精度和成本效益。随着技术的进步,对利用人工智能应用程序的专业软件知识的需求正在减少。我们的研究将研究在免疫学领域采用基于AI的图像分析的优势和挑战,并将调查没有软件专业知识的医生是否可以使用MSAzurePortal进行ANAIIF测试分类和图像分析。这是第一项使用MSAzure门户执行Hep-2图像分析的研究。我们还将评估AI应用的潜力,以帮助医生在免疫学实验室中解释ANAIIF结果。这项研究是由两位专家分四个阶段设计的。阶段1:创建图像库,第二阶段:寻找人工智能应用,第三阶段:上传图像和训练人工智能,第四阶段:人工智能应用的性能分析。在第一次训练中,72张测试图像的平均模式识别准确率为81.94%。第二次训练后,这一准确度提高到87.5%。第二次训练后,模式精度从71.42提高到79.96%。因此,正确识别的模式的数量及其准确性随着第二次训练过程而增加。基于人工智能的图像分析显示出巨大的潜力。这项技术预计将成为医疗机构实验室必不可少的。提供更高的准确率和更低的成本。
Artificial intelligence (AI) is increasingly being used in medicine to enhance the speed and accuracy of disease diagnosis and treatment. AI-based image analysis is expected to play a crucial role in future healthcare facilities and laboratories, offering improved precision and cost-effectiveness. As technology advances, the requirement for specialized software knowledge to utilize AI applications is diminishing. Our
study will examine the advantages and challenges of employing AI-based image analysis in the field of immunology and will investigate whether physicians without software expertise can use MS Azure Portal for ANA IIF test classification and image analysis. This is the first
study to perform Hep-2 image analysis using MS Azure Portal. We will also assess the potential for AI applications to aid physicians in interpreting ANA IIF results in immunology laboratories. The
study was designed in four stages by two specialists. Stage 1: creation of an image library, Stage 2: finding an artificial intelligence application, Stage 3: uploading images and training artificial intelligence, Stage 4: performance analysis of the artificial intelligence application. In the first training, the average pattern identification accuracy for 72 testing images was 81.94%. After the second training, this accuracy increased to 87.5%. Patterns Precision improved from 71.42 to 79.96% after the second training. As a result, the number of correctly identified patterns and their accuracy increased with the second training process. Artificial intelligence-based image analysis shows promising potential. This technology is expected to become essential in healthcare facility laboratories, offering higher accuracy rates and lower costs.