迈向AI辅助家禽球虫病诊断：评估Gemini和BiomedParse在艾美耳球虫显微镜图像上的表现
Toward AI-Assisted Poultry Coccidiosis Diagnosis: Evaluating Gemini and BiomedParse on Eimeria Microscopy Images
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中文总结 AI 辅助
本研究评估通用多模态大模型Gemini和BiomedParse在艾美耳球虫显微镜图像诊断中的表现，发现其准确率低且存在偏差，表明当前模型需领域微调和专家验证才能用于家禽球虫病诊断。
中文摘要 AI 辅助
由艾美耳球虫（Eimeria）寄生虫引起的球虫病是家禽生产中的重大经济负担，有效控制依赖于准确的物种级诊断。本研究评估通用多模态大语言模型能否支持此类诊断。研究在两种提示条件下，对Google Gemini在覆盖七种感染家禽的艾美耳球虫物种的4,225张显微镜图像上进行了评估，一种条件不提供候选标签，另一种提供预定义类别列表，并进一步测试了其病理报告生成能力，同时考察了BiomedParse在寄生虫分割方面的表现。在不提供候选标签的情况下，模型产生了宽泛且分类学上不一致的输出。在提供候选标签的情况下，总体准确率仅为14.9%，且对E. tenella存在强烈偏向，占比74%，对E. acervulina、E. mitis和E. praecox则完全没有正确分类。生成的报告连贯但未经核实，分割结果仅为部分。因此，当前多模态模型在没有领域特定微调和专家验证的情况下，尚不能可靠地独立用于艾美耳球虫诊断。
英文摘要
Coccidiosis caused by Eimeria parasites is a major economic burden in poultry production, and effective control depends on accurate species-level diagnosis. This study evaluates whether a general-purpose multimodal large language model can support such diagnosis. Google Gemini was assessed on 4,225 mi- croscopy images covering the seven fowl-infecting Eimeria species under two prompting conditions, one without candidate labels and one with a predefined class list, and was further tested for pathology-report generation, while BiomedParse was examined for parasite segmentation. Without candidate labels, the model produced broad and taxonomically inconsistent outputs. With candidate labels, overall accuracy reached only 14.9%, with a strong bias toward E. tenella at 74% and no correct classifications for E. acervulina, E. mitis and E. praecox. Generated treatment reports were coherent but unverified, and segmentation was only partial. Current multimodal models are therefore not yet reliable for standalone Eimeria diagnosis without domain-specific fine- tuning and expert validation.
