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利用人工智能和聚合样本监测多微生物感染猪群中的呼吸道疾病模式。

Monitoring of Respiratory Disease Patterns in a Multimicrobially Infected Pig Population Using Artificial Intelligence and Aggregate Samples.

机构信息

Clinic for Swine at the Centre for Clinical Veterinary Medicine, Ludwig-Maximilians-University München, 85764 München, Germany.

Boehringer Ingelheim Vetmedica GmbH, Ingelheim, 55216 Ingelheim am Rhein, Germany.

出版信息

Viruses. 2024 Oct 6;16(10):1575. doi: 10.3390/v16101575.

DOI:10.3390/v16101575
PMID:39459909
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11512249/
Abstract

A 24/7 AI sound-based coughing monitoring system was applied in combination with oral fluids (OFs) and bioaerosol (AS)-based screening for respiratory pathogens in a conventional pig nursery. The objective was to assess the additional value of the AI to identify disease patterns in association with molecular diagnostics to gain information on the etiology of respiratory distress in a multimicrobially infected pig population. Respiratory distress was measured 24/7 by the AI and compared to human observations. Screening for swine influenza A virus (swIAV), porcine reproductive and respiratory disease virus (PRRSV), , , and porcine circovirus 2 (PCV2) was conducted using qPCR. Except for , all of the investigated pathogens were detected within the study period. High swIAV-RNA loads in OFs and AS were significantly associated with a decrease in respiratory health, expressed by a respiratory health score calculated by the AI The odds of detecting PRRSV or were significantly higher for OFs compared to AS. qPCR examinations of OFs revealed significantly lower Ct-values for swIAV and compared to AS. In addition to acting as an early warning system, AI gained respiratory health data combined with laboratory diagnostics, can indicate the etiology of respiratory distress.

摘要

24/7 人工智能基于声音的咳嗽监测系统与口腔液（OFs）和基于生物气溶胶（AS）的呼吸道病原体筛选相结合，应用于常规猪舍。目的是评估人工智能的附加价值，以识别与分子诊断相关的疾病模式，从而了解多微生物感染猪群呼吸窘迫的病因。通过人工智能 24/7 测量呼吸窘迫，并与人工观察进行比较。使用 qPCR 对猪流感 A 病毒（swIAV）、猪繁殖与呼吸综合征病毒（PRRSV）、猪传染性胃肠炎病毒（TGEV）、猪德尔塔冠状病毒（PDCoV）和猪圆环病毒 2 （PCV2）进行了筛查。除了 PDCoV，在所研究的时间段内都检测到了所有调查的病原体。高水平的 OFs 和 AS 中的 swIAV-RNA 载量与呼吸健康下降显著相关，通过人工智能计算的呼吸健康评分来表示。与 AS 相比，检测到 PRRSV 或 TGEV 的可能性显著更高。OFs 的 qPCR 检查显示，与 AS 相比，swIAV 和 TGEV 的 Ct 值显著降低。除了作为早期预警系统外，人工智能结合实验室诊断获得的呼吸健康数据，可以指示呼吸窘迫的病因。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10e9/11512249/7aac728bc4c1/viruses-16-01575-g007.jpg
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