用于训练图像集开发的选定蠕虫卵观察者间解读的变异性

Variability of interobserver interpretation of selected helminth ova in the development of a training image set.

作者信息

Chua Rupert Stephen Charles S, Henderson Kiersten A, de Guzman Lorenzo Maria C, Foss Vicki, Schub Nathaniel, Bell Cameron, Medina John Robert C, Siao Taggart G, Mistica Myra S, Belleza Maria Luz B, Modequillo Marie Cris R, Torres Nadine Joyce C, Belizario Vicente Y

机构信息

Neglected Tropical Diseases Study Group, National Institutes of Health, University of the Philippines Manila, Manila, Philippines.

Parasite ID, Corp., Seattle, WA, USA.

出版信息

Int Health. 2025 Sep 3;17(5):836-842. doi: 10.1093/inthealth/ihae085.

DOI:10.1093/inthealth/ihae085

PMID:39782878

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12406772/

Abstract

BACKGROUND

Diagnosis of soil-transmitted helminthiasis and schistosomiasis for surveillance relies on microscopic detection of ova in Kato-Katz (KK) prepared slides. Artificial intelligence (AI)-based platforms for parasitic eggs may be developed using a robust image set with defined labels by reference microscopists. This study aimed to determine interobserver variability among reference microscopists in identifying parasite ova.

METHODS

Images of parasite ova taken from KK prepared slides were labelled according to species by two reference microscopists (M1 and M2). A third reference microscopist (M3) labelled images when the first two did not agree. Frequency, percent agreement, κ statistics and variability score (VS) were generated for analysis.

RESULTS

M1 and M2 agreed on 89.24% of the labelled images (κ=0.86, p<0.001). M3 had agreement with M1 and M2 (κ=0.30, p<0.001 and κ=0.28, p<0.001), resolving 89.29% of disagreement between them. The labelling of Schistosoma japonicum had the highest VS (κ=0.487, p=0.101) among the targeted ova. Reference microscopists were able to reliably reach consensus in 99.0% of the dataset.

CONCLUSIONS

Training AI using this image set may provide more objective and reliable readings compared with that of reference microscopists.

摘要

背景

用于监测的土壤传播蠕虫病和血吸虫病的诊断依赖于在加藤厚涂片（KK）制备的载玻片上通过显微镜检测虫卵。基于人工智能（AI）的寄生虫卵检测平台可以利用参考显微镜专家提供的带有明确标签的强大图像集来开发。本研究旨在确定参考显微镜专家在识别寄生虫卵方面的观察者间变异性。

方法

从KK制备的载玻片上获取的寄生虫卵图像由两名参考显微镜专家（M1和M2）按种类进行标记。当M1和M2意见不一致时，由第三名参考显微镜专家（M3）对图像进行标记。生成频率、一致百分比、κ统计量和变异性评分（VS）用于分析。

结果

M1和M2对89.24%的标记图像达成一致（κ=0.86，p<0.001）。M3与M1和M2的一致性分别为（κ=0.30，p<0.001和κ=0.28，p<0.001），解决了他们之间89.29%的分歧。在目标虫卵中，日本血吸虫的标记VS最高（κ=0.487，p=0.101）。参考显微镜专家在99.0%的数据集中能够可靠地达成共识。