Development of an artificial intelligence-based multimodal model for assisting in the diagnosis of necrotizing enterocolitis in newborns: a retrospective study.

Objective: The purpose of this study is to develop a multimodal model based on artificial intelligence to assist clinical doctors in the early diagnosis of necrotizing enterocolitis in newborns. Methods: This study is a retrospective study that collected the initial laboratory test results and abdominal x-ray image data of newborns (non-NEC, NEC) admitted to our hospital from January 2022 to January 2024.A multimodal model was developed to differentiate multimodal data, trained on the training dataset, and evaluated on the validation dataset. The interpretability was enhanced by incorporating the Gradient-weighted Class Activation Mapping (GradCAM) analysis to analyze the attention mechanism of the multimodal model, and finally compared and evaluated with clinical doctors on external datasets. Results: The dataset constructed in this study included 11,016 laboratory examination data from 408 children and 408 image data. When applied to the validation dataset, the area under the curve was 0.91, and the accuracy was 0.94. The GradCAM analysis shows that the model's attention is focused on the fixed dilatation of the intestinal folds, intestinal wall edema, interintestinal gas, and portal venous gas. External validation demonstrated that the multimodal model had comparable accuracy to pediatric doctors with ten years of clinical experience in identification. Conclusion: The multimodal model we developed can assist doctors in early and accurate diagnosis of NEC, providing a new approach for assisting diagnosis in underdeveloped medical areas.

Typing of bronchiectasis according to syndrome differentiation / 中西医结合学报

OBJECTIVE: To study the general law of typing of bronchiectasis according to syndrome differentiation. METHODS: We collected the symptoms, conditions of tongue and pulse in patients of bronchiectasis, using frequencies procedure, discriminant analysis and K-means cluster analysis in SPSS statistical software as research medium. RESULTS: Five hundred and sixty three patients with bronchiectasis were studied. It suggested that accumulation of phlegm-heat in the lungs (45.65%), liver fire attacking the lungs (24.51%), asthenia of pulmonosplenic qi (22.38%), asthenia of both qi and yin (7.46%) were the main types. CONCLUSION: Clinical epidemiology provided scientific basis for further studying of the typing of bronchiectasis according to syndrome differentiation. Building up differentiation of syndromes through differentiation and analysis of main symptoms can be used in clinical diagnosis.