Early disease detection in veterinary care relies on identifying subclinical
abnormalities in asymptomatic animals during wellness visits. This study
introduces an algorithm designed to distinguish between wellness and other
veterinary visits.The purpose of this study is to validate the use of a visit
classification algorithm compared to manual classification of veterinary visits
by three board-certified veterinarians. Using a dataset of 11,105 clinical
visits from 2012 to 2017 involving 655 animals (85.3% canines and 14.7%
felines) across 544 U.S. veterinary establishments, the model was trained using
a Gradient Boosting Machine model. Three validators were tasked with
classifying 400 visits, including both wellness and other types of visits,
selected randomly from the same database used for initial algorithm training,
aiming to maintain consistency and relevance between the training and
application phases; visit classifications were subsequently categorized into
"wellness" or "other" based on majority consensus among validators to assess
the algorithm's performance in identifying wellness visits. The algorithm
demonstrated a specificity of 0.94 (95% CI: 0.91 to 0.96), implying its
accuracy in distinguishing non-wellness visits. The algorithm had a sensitivity
of 0.86 (95% CI: 0.80 to 0.92), indicating its ability to correctly identify
wellness visits as compared to the annotations provided by veterinary experts.
The balanced accuracy, calculated as 0.90 (95% CI: 0.87 to 0.93), further
confirms the algorithm's overall effectiveness. The algorithm exhibits strong
specificity and sensitivity, ensuring accurate identification of a high
proportion of wellness visits. Overall, this algorithm holds promise for
advancing research on preventive care's role in subclinical disease
identification, but prospective studies are needed for validation.

这项研究介绍了一种用于区分诊疗访问类型的算法，并验证了该算法在识别保健访问中的有效性。该算法具有较高的准确性，可用于提高亚临床疾病识别中预防保健的研究，但需要进行进一步的有效性验证。

猫狗临床保健访问分类的机器学习算法开发与验证

Development and Validation of a Machine Learning Algorithm for Clinical  Wellness Visit Classification in Cats and Dogs

Evaluating canine electrocardiograms (ECG) require skilled veterinarians, but
current availability of veterinary cardiologists for ECG interpretation and
diagnostic support is limited. Developing tools for automated assessment of ECG
sequences can improve veterinary care by providing clinicians real-time results
and decision support tools. We implement a deep convolutional neural network
(CNN) approach for classifying canine electrocardiogram sequences as either
normal or abnormal. ECG records are converted into 8 second Lead II sequences
and classified as either normal (no evidence of cardiac abnormalities) or
abnormal (presence of one or more cardiac abnormalities). For training ECG
sequences are randomly augmented using RandomAugmentECG, a new augmentation
library implemented specifically for this project. Each chunk is then is
converted using a continuous wavelet transform into a 2D scalogram. The 2D
scalogram are then classified as either normal or abnormal by a binary CNN
classifier. Experimental results are validated against three boarded veterinary
cardiologists achieving an AUC-ROC score of 0.9506 on test dataset matching
human level performance. Additionally, we describe model deployment to
Microsoft Azure using an MLOps approach. To our knowledge, this work is one of
the first attempts to implement a deep learning model to automatically classify
ECG sequences for canines.Implementing automated ECG classification will
enhance veterinary care through improved diagnostic performance and increased
clinic efficiency.

使用深度卷积神经网络 (CNN) 方法对犬的心电图序列进行分类研究，以诊断出是否出现心脏异常状况，以提高兽医的诊断性能，从而实现完善的兽医护理。