Perioperative anesthetic management entails a multitude of decision-making processes within complex medical scenarios. These demand the continuous and dynamic execution of precise decisions which poses significant challenges. In the age of big data, the exponential growth in data volume from diverse sources has revolutionized many fields, including healthcare, finance, and marketing. Machine learning has emerged as a powerful tool for analyzing big data, enabling the handling of large datasets and uncovering intricate patterns and relationships. The application of big data and artificial intelligence algorithms is gradually being integrated, enabling effective task completion in various stages of perioperative management, including risk prediction, decision support, and auxiliary examination. Through in-depth analysis of big data, healthcare professionals can gain insights into patient prognoses. This review provides a comprehensive overview of the distinctive features of perioperative big data and its applications in anesthesia management during the perioperative period.

Introduction: Though the importance raised attention, the clinical applications of methods for screening high-risk patients of sepsis after abdominal surgery were restricted. Therefore, we aimed to develop and validate models to screening high-risk patients of sepsis after abdominal surgery based on machine learning with routine variables.Material and methods: The whole dataset was composed of three representative academic hospitals in China and Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Routine clinical variables were implemented for model development. Boruta was applied for feature selection. Afterwards, ensemble learning and other eight conventional algorithms were used for model fitting and validation based on all features and selected features. The area under curves of the receiver operating characteristic curves (ROCAUCs), sensitivity, specificity, F1 score, accuracy, Net reclassification index (NRI), integrated discrimination improvement (IDI), Decision Curve Analysis (DCA), and calibration curves were used for model evaluation.Results: A total of 955 patients undergoing abdominal surgery were finally analyzed (sepsis:285, non-sepsis:670). After feature selection, the ensemble learning model constructed by integrating k-Nearest Neighbor (KNN) and Support Vector Machine (SVM), yielded the ROCAUC of 0.892(0.841-0.944), the accuracy of 85.0% on the test data, and the ROCAUC of 0.782(0.727-0.838), the accuracy of 68.1% on the validation data, which performed best. Albumin, ASA score, Neutrophil-lymphocyte ratio, age, and glucose were the top features associated with postoperative sepsis by KNN and SVM.Conclusions: We developed a new and potential generalizable model to preoperatively screening the high-risk patients of sepsis after abdominal surgery with the advantages of a representative training cohort and routine variables.

Background The context and purpose of this study is to introduce a novel, compact, and efficient system that leverages deep learning and smartphone technology to estimate hemoglobin levels. Accurate measurement of hemoglobin concentration is essential for various medical scenarios, including preoperative evaluations and determining blood loss. Current models, due to their complex parameters, are not well-suited for mobile medical settings, which limits the ability to conduct frequent and rapid testing. Methods The study employed a smartphone application to capture images of the eye, which were then analyzed by a deep neural network trained using invasive blood test data. For the task of eyelid segmentation, the EGE-Unet model was used. The performance of this model was evaluated using statistical metrics including mean intersection over union (MIOU), F1 Score, accuracy, specificity, and sensitivity. For hemoglobin level prediction, the DHA(C3AE) model was employed. The performance of this model was assessed using mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and R^2. Results The EGE-Unet model demonstrated robust performance in eyelid segmentation, achieving a MIOU of 0.78, an F1 Score of 0.87, an accuracy of 0.97, a specificity of 0.98, and a sensitivity of 0.86. The DHA(C3AE) model for hemoglobin level prediction yielded promising outcomes with a MAE of 1.34, a MAPE of 2.85, an RMSE of 1.69, and an R^2 of 0.34. The overall size of the model is modest at 1.08M, with a computational complexity of 0.12 FLOPs (G). Conclusions This groundbreaking approach eliminates the necessity for supplementary devices, providing a cost-effective, swift, and accurate method for healthcare professionals to enhance treatment planning and improve patient care in perioperative environments. The proposed system has the potential to enable frequent and rapid testing of hemoglobin levels, which can be particularly beneficial in mobile medical settings. Trial Registration: The clinical trial was registered on the Chinese Clinical Trial Registry (No. ChiCTR2100044138) on March 11, 2021.