Department of Microbiology, Melmaruvathur Adhiparasakthi Institute of Medical Sciences and Research, India.
Stud Health Technol Inform. 2022 Nov 3;299:202-207. doi: 10.3233/SHTI220984.
Anti-Microbial Resistance is one of the greatest threats that mankind faces right now due to the inappropriate use of antibiotics. Institution of appropriate antibiotics in right dose for the right patient at right time is the "gamechanger" in fighting AMR. Antibiotic Sensitivity Testing (AST) or antibiogram is done to ascertain the sensitivity profile of the organism. The most widely used method in laboratory practice in India is the Kirby-Bauer's disk diffusion test. There are few shortcomings in the manual interpretation of antibiograms in the form of high inter-operator variability, mandatory requirement of trained microbiologists - which is difficult in low-resource settings and high degree of interpersonal bias due to various factors like stress, workload, and visual acuity. We propose the Ab.ai tool for automating the AST procedures in laboratory. The Ab.ai tool comprises of 3 phases: first for data collection, second for data processing and the third for generation of antibiotic sensitivity reports. Various software packages like OpenCV and EasyOCR are used for the development of the Ab.ai tool. A total of 50 antibiograms of both GPC and GNB are interpreted both by manual and automated method. The manual method is considered the "gold-standard" and the performance of Ab.ai tool was compared against the manual method. The Ab.ai tool achieved an agreement of 98.4% on susceptibility categorization of GPC antibiotics and 97.6% on that of GNB antibiotics against the gold standard manual method. The proposed Ab.ai tool serves as a perfect candidate for automating AST procedures and would prove to be a "game-changer" in battling AMR.
由于抗生素的不当使用,抗微生物药物耐药性是当前人类面临的最大威胁之一。在适当的时间为适当的患者给予适当剂量的抗生素是对抗 AMR 的“游戏规则改变者”。抗生素敏感性测试(AST)或药敏试验用于确定生物体的敏感性特征。在印度,实验室实践中最广泛使用的方法是 Kirby-Bauer 圆盘扩散试验。在以高操作员间变异性为特征的手动解释药敏试验形式中存在一些缺点,需要经过培训的微生物学家的强制性要求-这在资源匮乏的环境中很困难,并且由于各种因素(如压力、工作量和视力)导致人际偏见程度很高。我们提出了 Ab.ai 工具,用于自动化实验室中的 AST 程序。Ab.ai 工具包括 3 个阶段:第一阶段用于数据收集,第二阶段用于数据处理,第三阶段用于生成抗生素敏感性报告。各种软件包,如 OpenCV 和 EasyOCR,用于开发 Ab.ai 工具。总共解释了 50 份 GPC 和 GNB 的药敏试验,分别采用手动和自动方法。手动方法被认为是“金标准”,并将 Ab.ai 工具的性能与手动方法进行了比较。Ab.ai 工具在 GPC 抗生素的敏感性分类上达到了 98.4%的一致性,在 GNB 抗生素的敏感性分类上达到了 97.6%的一致性,与金标准手动方法相比。拟议的 Ab.ai 工具是自动化 AST 程序的理想候选者,并将成为对抗 AMR 的“游戏规则改变者”。