Artificial intelligence applications for immunology laboratory: image analysis and classification study of IIF photos.

IF 3.3 4区 医学 Q3 IMMUNOLOGY
Immunologic Research Pub Date : 2024-12-01 Epub Date: 2024-08-06 DOI:10.1007/s12026-024-09527-z
Mehmet Akif Durmuş, Selda Kömeç, Abdurrahman Gülmez
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引用次数: 0

Abstract

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.

Abstract Image

免疫学实验室的人工智能应用:IIF 照片的图像分析和分类研究。
人工智能(AI)正越来越多地应用于医学领域,以提高疾病诊断和治疗的速度和准确性。基于人工智能的图像分析预计将在未来的医疗设施和实验室中发挥关键作用,提供更高的精确度和成本效益。随着技术的进步,利用人工智能应用对专业软件知识的要求也在降低。我们的研究将探讨在免疫学领域应用基于人工智能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 MS Azure Portal 进行 ANA IIF 检测分类和图像分析。这是第一项使用 MS Azure Portal 进行 Hep-2 图像分析的研究。我们还将评估人工智能应用的潜力,以帮助医生解释免疫学实验室的 ANA IIF 结果。这项研究由两位专家分四个阶段设计。第一阶段:创建图像库;第二阶段:寻找人工智能应用程序;第三阶段:上传图像并训练人工智能;第四阶段:人工智能应用程序的性能分析。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,准确率提高到 87.5%。第二次训练后,模式精确度从 71.42% 提高到 79.96%。因此,随着第二次训练过程的进行,正确识别模式的数量和准确率都有所提高。基于人工智能的图像分析显示出巨大的潜力。这项技术有望成为医疗机构实验室的必备技术,提供更高的准确率和更低的成本。
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来源期刊
Immunologic Research
Immunologic Research 医学-免疫学
CiteScore
6.90
自引率
0.00%
发文量
83
审稿时长
6-12 weeks
期刊介绍: IMMUNOLOGIC RESEARCH represents a unique medium for the presentation, interpretation, and clarification of complex scientific data. Information is presented in the form of interpretive synthesis reviews, original research articles, symposia, editorials, and theoretical essays. The scope of coverage extends to cellular immunology, immunogenetics, molecular and structural immunology, immunoregulation and autoimmunity, immunopathology, tumor immunology, host defense and microbial immunity, including viral immunology, immunohematology, mucosal immunity, complement, transplantation immunology, clinical immunology, neuroimmunology, immunoendocrinology, immunotoxicology, translational immunology, and history of immunology.
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