Canine hypoadrenocorticism (CHA) is a life-threatening condition that affects approximately 3 of 1,000 dogs. It has a wide array of clinical signs and is known to mimic other disease processes, including kidney and gastrointestinal diseases, creating a diagnostic challenge. Because CHA can be fatal if not appropriately treated, there is risk to the patient if the condition is not diagnosed. However, the prognosis is excellent with appropriate therapy. A major hurdle to diagnosing CHA is the lack of awareness and low index of suspicion. Once suspected, the application and interpretation of conclusive diagnostic tests is relatively straight forward. In this study, machine learning methods were employed to aid in the diagnosis of CHA using routinely collected screening diagnostics (complete blood count and serum chemistry panel). These data were collected for 908 control dogs (suspected to have CHA, but disease ruled out) and 133 dogs with confirmed CHA. A boosted tree algorithm (AdaBoost) was trained with 80% of the collected data, and 20% was then utilized as test data to assess performance. Algorithm learning was demonstrated as the training set was increased from 0 to 600 dogs. The developed algorithm model has a sensitivity of 96.3% (95% CI, 81.7%–99.8%), specificity of 97.2% (95% CI, 93.7%–98.8%), and an area under the receiver operator characteristic curve of 0.994 (95% CI, 0.984–0.999), and it outperforms other screening methods including logistic regression analysis. An easy-to-use graphical interface allows the practitioner to easily implement this technology to screen for CHA leading to improved outcomes for patients and owners.

犬肾上腺皮质激素缺乏症（CHA）是一种威胁生命的疾病，大约影响1,000只狗中的3只。它具有广泛的临床体征，并且已知能模仿其他疾病过程，包括肾脏和胃肠道疾病，给诊断带来挑战。由于如果不适当治疗，CHA可能会致命，因此，如果未诊断出该病，则可能给患者带来风险。然而，适当的治疗预后极好。诊断CHA的主要障碍是缺乏意识和低怀疑度。一旦被怀疑，结论性诊断测试的应用和解释就相对简单了。在这项研究中，使用机器学习方法通​​过常规收集的筛查诊断（全血细胞计数和血清化学检测）来辅助诊断CHA。这些数据收集了908只对照犬（怀疑患有CHA，但已排除疾病）和133只确诊CHA的犬。使用收集的80％的数据对增强树算法（AdaBoost）进行了训练，然后将20％的数据用作测试数据以评估性能。训练集从0只增加到600只，证明了算法学习。所开发的算法模型的灵敏度为96.3％（95％CI，81.7％–99.8％），特异性为97.2％（95％CI，93.7％–98.8％），接收器操作员特征曲线下的面积为0.994（ 95％CI（0.984–0.999），并且胜过其他筛查方法，包括逻辑回归分析。易于使用的图形界面使从业人员可以轻松地实施该技术来筛查CHA，从而为患者和所有者改善结果。