Engel-Manchado Javier, Montoya-Alonso José Alberto, Doménech Luis, Monge-Utrilla Oscar, Reina-Doreste Yamir, Matos Jorge Isidoro, Caro-Vadillo Alicia, García-Guasch Laín, Redondo José Ignacio
Internal Medicine, Veterinary Medicine and Therapeutic Research Group, Faculty of Veterinary Science, Research Institute of Biomedical and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain.
Cardiology Service, AniCura Benipeixcar Veterinary Hospital, 46009 Valencia, Spain.
Vet Sci. 2024 Mar 6;11(3):118. doi: 10.3390/vetsci11030118.
Myxomatous mitral valve disease (MMVD) is a prevalent canine cardiac disease typically diagnosed and classified using echocardiography. However, accessibility to this technique can be limited in first-opinion clinics. This study aimed to determine if machine learning techniques can classify MMVD according to the ACVIM classification (B1, B2, C, and D) through a structured anamnesis, quality of life survey, and physical examination. This report encompassed 23 veterinary hospitals and assessed 1011 dogs for MMVD using the FETCH-Q quality of life survey, clinical history, physical examination, and basic echocardiography. Employing a classification tree and a random forest analysis, the complex model accurately identified 96.9% of control group dogs, 49.8% of B1, 62.2% of B2, 77.2% of C, and 7.7% of D cases. To enhance clinical utility, a simplified model grouping B1 and B2 and C and D into categories B and CD improved accuracy rates to 90.8% for stage B, 73.4% for stages CD, and 93.8% for the control group. In conclusion, the current machine-learning technique was able to stage healthy dogs and dogs with MMVD classified into stages B and CD in the majority of dogs using quality of life surveys, medical history, and physical examinations. However, the technique faces difficulties differentiating between stages B1 and B2 and determining between advanced stages of the disease.
黏液瘤样二尖瓣疾病（MMVD）是一种常见的犬类心脏疾病，通常使用超声心动图进行诊断和分类。然而，在初诊诊所中，这种技术的可及性可能有限。本研究旨在确定机器学习技术是否能够通过结构化问诊、生活质量调查和体格检查，根据美国兽医内科学会（ACVIM）的分类（B1、B2、C和D）对MMVD进行分类。本报告涵盖了23家兽医医院，使用FETCH-Q生活质量调查问卷、临床病史、体格检查和基本超声心动图对1011只犬进行了MMVD评估。采用分类树和随机森林分析，复杂模型准确识别出96.9%的对照组犬、49.8%的B1期犬、62.2%的B2期犬、77.2%的C期犬和7.7%的D期犬。为了提高临床实用性，一个简化模型将B1和B2以及C和D归为B类和CD类，B期的准确率提高到90.8%，CD期为73.4%，对照组为93.8%。总之，当前的机器学习技术能够通过生活质量调查、病史和体格检查，对大多数健康犬以及被分类为B期和CD期的MMVD犬进行分期。然而，该技术在区分B1和B2期以及确定疾病晚期方面面临困难。
