RESUMEN
BACKGROUND: The incidence of asthma in Chinese children has rapidly increased as a result of inadequate management. This is mainly due to the failure of many primary-level pediatricians to distinguish asthma from common respiratory diseases, such as bronchitis and pneumonia. Such misdiagnoses often lead to the abuse of antibiotics and systemic glucocorticoids. Additionally, if asthma is not diagnosed early, chronic airway inflammation results in lesions that not only hamper children's athletic abilities, but serve as the primary cause for adult chronic airway diseases, such as chronic obstructive pulmonary disease (COPD). METHODS: A number of machine learning-based models including CatBoost, Logistic Regression, Naïve Bayes, and Support Vector Machines (SVM) have been developed to identify asthma via utilizing retrospective electronic medical records (EMRs) of patients. These models were evaluated independently using EMRs from both the Pulmonology Department and other departments of the Children's Hospital, Zhejiang University School of Medicine, China. RESULTS: Two independent test sets were applied for performance evaluation. TestSet-1 consisted of 325 positive asthma cases and 428 negative cases from the Pulmonology Department. TestSet-2 was composed of 2,123 cases from non-pulmonology departments, and included 337 positive and 1,786 negative cases. Experimental results showed that the CatBoost model outperformed other models on both test sets with an accuracy of 84.7% and an area under the curve (AUC) of 90.9% on TestSet-1, and an accuracy of 96.7% and an AUC of 98.1% on TestSet-2. CONCLUSIONS: The artificial intelligence (AI) model could rapidly and accurately identify asthma in general medical wards of children, and may aid primary pediatricians in the correct diagnoses of asthma. It possesses great clinical value and practical significance in improving the control rate of asthma in children, optimizing medical resources, and limiting the abuse of antibiotics and systemic glucocorticoids.
RESUMEN
BACKGROUND: As the booming of deep learning era, especially the advances in convolutional neural networks (CNNs), CNNs have been applied in medicine fields like radiology and pathology. However, the application of CNNs in dermatology, which is also based on images, is very limited. Inflammatory skin diseases, such as psoriasis (Pso), eczema (Ecz), and atopic dermatitis (AD), are very easily to be mis-diagnosed in practice. METHODS: Based on the EfficientNet-b4 CNN algorithm, we developed an artificial intelligence dermatology diagnosis assistant (AIDDA) for Pso, Ecz & AD and healthy skins (HC). The proposed CNN model was trained based on 4,740 clinical images, and the performance was evaluated on experts-confirmed clinical images grouped into 3 different dermatologist-labelled diagnosis classifications (HC, Pso, Ecz & AD). RESULTS: The overall diagnosis accuracy of AIDDA is 95.80%±0.09%, with the sensitivity of 94.40%±0.12% and specificity 97.20%±0.06%. AIDDA showed accuracy for Pso is 89.46%, with sensitivity of 91.4% and specificity of 95.48%, and accuracy for AD & Ecz 92.57%, with sensitivity of 94.56% and specificity of 94.41%. CONCLUSIONS: AIDDA is thus already achieving an impact in the diagnosis of inflammatory skin diseases, highlighting how deep learning network tools can help advance clinical practice.