School of Medical Sciences, Universidade Estadual de Campinas, Campinas, SP, Brazil.
Laboratory of Image Data Science (LIDS), Institute of Computing (IC), Universidade Estadual de Campinas, Campinas, SP, Brazil.
Parasit Vectors. 2024 Aug 30;17(1):368. doi: 10.1186/s13071-024-06434-y.
Techniques for diagnosing intestinal parasites need technological advancements in the preanalytical (collection/processing) and analytical (detection) stages. The dissolved air flotation (DAF) technique effectively recovers parasites from processed feces for routine diagnosis. Artificial intelligence (AI) is a practical and affordable alternative to modernize the analysis stage of microscopy images and generates high efficiency in the parasitological examination of feces.
The objective of this study was to standardize a laboratory protocol for stool processing using the DAF technique in conjunction with an automated diagnosis of intestinal parasites (DAPI) system. A total of 400 samples were obtained to perform the tests with the use of DAF to verify the recovery of the parasites as a function of the chemical reagent (polymer and surfactant), the volume of the flotation tube, and standardization of smear assembly on a microscopy slide, with automated analysis by DAPI. The DAF protocol that obtained the most satisfactory results in terms of parasite recovery (P < 0.05) and slide positivity was compared with the Three Fecal Test (TF-Test) protocol with manual (microscopists) and automated (DAPI) evaluation. We compared the sensitivity with the modified TF-Test technical protocol and the diagnostic agreement with the gold standard (Kappa) result.
There was no significant difference in the parasite recovery between the 10 ml and 50 ml tubes (P > 0.05). The surfactants showed a range of parasite recoveries between 41.9% and 91.2% in the float supernatant. We obtained a maximum positivity of 73% of the assembled slides when we applied DAF processing with 7% CTAB surfactant and 57% positivity with the modified TF-Test technique. Regarding diagnostic performance, the TF-Test-modified and DAF techniques used in fecal processing for subsequent computerized analysis by AI presented sensitivities of 86% and 94%, with kappa agreements of 0.62 and 0.80 (substantial), respectively.
The DAF protocol defined in this study and the DAPI system are innovative processes for parasite recovery and fecal debris elimination that are favorable for effectively detecting pathogenic structures in laboratory diagnosis.
诊断肠道寄生虫的技术需要在预分析（采集/处理）和分析（检测）阶段进行技术进步。溶解空气浮选（DAF）技术可有效地从处理过的粪便中回收寄生虫，用于常规诊断。人工智能（AI）是一种实用且经济实惠的替代方案，可以实现显微镜图像分析阶段的现代化，并在粪便寄生虫检查中提高效率。
本研究的目的是标准化使用 DAF 技术结合自动化肠道寄生虫诊断（DAPI）系统处理粪便的实验室方案。共获得 400 个样本进行测试，使用 DAF 验证寄生虫的回收率作为化学试剂（聚合物和表面活性剂）、浮选管体积和显微镜载玻片上的涂片组装标准化的函数，通过 DAPI 进行自动化分析。比较了在寄生虫回收率（P<0.05）和玻片阳性率方面获得最满意结果的 DAF 方案与手动（显微镜专家）和自动（DAPI）评估的三粪检（TF-Test）方案。我们比较了改良 TF-Test 技术方案的敏感性和金标准（Kappa）结果的诊断一致性。
10ml 和 50ml 管之间的寄生虫回收率没有差异（P>0.05）。表面活性剂在浮渣上清液中的寄生虫回收率范围为 41.9%至 91.2%。当我们使用 7%CTAB 表面活性剂进行 DAF 处理并将改良 TF-Test 技术的阳性率提高到 57%时，我们获得了组装载玻片的最大阳性率为 73%。关于诊断性能，改良 TF-Test 和 DAF 技术在粪便处理中用于后续人工智能计算机化分析，其敏感性分别为 86%和 94%，Kappa 一致性分别为 0.62 和 0.80（高度一致）。
本研究中定义的 DAF 方案和 DAPI 系统是用于寄生虫回收和粪便碎屑消除的创新过程，有利于在实验室诊断中有效检测致病结构。
