Miniaturized Heart-Lung Machine

The miniaturized heart-lung machine consists of commercially available roller pumps, a flexible heat exchanger, a newly devised bubble oxygenator, and polyethylene cannulas and silicone tubes. The minimum and maximal priming volume of the entire system is 4.7 and 16.7 mL, respectively. The efficiency of the system is reflected in an heat transfer coefficient ranging from 0.96 to 0.31 at flow rates between 1 and 20 mL/min, a high value of oxygen uptake in the range of 0.061 mL O₂/min mL blood^?1, and low blood trauma with plasma hemoglobin concentrations of 47.5 ± 5.0 mg/dL after 60 min of in vitro perfusion. The system is a simple, reliable, and efficient miniaturized heart-lung machine for use in small animals.     

Outcomes of Donation After Circulatory Death Heart Transplantation in Australia

Background

Transplantation of hearts retrieved from donation after circulatory death (DCD) donors is an evolving clinical practice.

Reverse engineering techniques applied to a human skull,for CAD 3D reconstruction and physical replication by rapid prototyping

The production of a copy of an existing object of complex shape is one of the typical applications of the integration between two modern computer-based technologies, reverse engineering (RE) and rapid prototyping (RP). The method is extremely versatile and can be used in various applicative domains (e.g. replacement of anatomical parts with artificial prostheses, replication of skeletal remains). Two different acquisition techniques of images of a skull, by laser and by CT scan, were compared to ascertain which enabled more accurate reproduction of the original specimen. The skull was chosen due to it being the body part most often used in medico-legal investigations (for personal identification, skull-photo superimposition techniques, forensic art, etc). Comparison between the copy and the original yielded satisfactory results for both techniques. However, CT scanning demonstrated some advantages over the laser technique, as it provided a cleaner point cloud, enabling shorter pre-reproduction processing times, as well as data on the internal parts, which resulted in the reproduction of a more faithful copy.     

Input representations and classification strategies for automated human gait analysis

BackgroundQuantitative gait analysis produces a vast amount of data, which can be difficult to analyze. Automated gait classification based on machine learning techniques bear the potential to support clinicians in comprehending these complex data. Even though these techniques are already frequently used in the scientific community, there is no clear consensus on how the data need to be preprocessed and arranged to assure optimal classification accuracy outcomes.Research questionIs there an optimal data aggregation and preprocessing workflow to optimize classification accuracy outcomes?MethodsBased on our previous work on automated classification of ground reaction force (GRF) data, a sequential setup was followed: firstly, several aggregation methods – early fusion and late fusion – were compared, and secondly, based on the best aggregation method identified, the expressiveness of different combinations of signal representations was investigated. The employed dataset included data from 910 subjects, with four gait disorder classes and one healthy control group. The machine learning pipeline comprised principle component analysis (PCA), z-standardization and a support vector machine (SVM).ResultsThe late fusion aggregation, i.e., utilizing majority voting on the classifier's predictions, performed best. In addition, the use of derived signal representations (relative changes and signal differences) seems to be advantageous as well.SignificanceOur results indicate that great caution is needed when data preprocessing and aggregation methods are selected, as these can have an impact on classification accuracies. These results shall serve future studies as a guideline for the choice of data aggregation and preprocessing techniques to be employed.     

Predictors of Patient Satisfaction Following Primary Total Knee Arthroplasty: Results from a Traditional Statistical Model and a Machine Learning Algorithm

BackgroundIt is well-documented in the orthopedic literature that 1 in 5 patients are dissatisfied following total knee arthroplasty (TKA). However, multiple statistical models have failed to explain the causes of dissatisfaction. Furthermore, payers are interested in using patient-reported satisfaction scores to adjust surgeon reimbursement rates without a full understanding of the influencing parameters. The purpose of this study was to more comprehensively identify predictors of satisfaction and compare results using both a statistical model and a machine learning (ML) algorithm.MethodsA retrospective review of consecutive TKAs performed by 2 surgeons was conducted. Identical perioperative protocols were utilized by both surgeons. Patients were grouped as satisfied or unsatisfied based on self-reported satisfaction scores. Fifteen variables were correlated with satisfaction using binary logistic regression and stochastic gradient boosted ML models.ResultsIn total, 1325 consecutive TKAs were performed. After exclusions, 897 TKAs were available with minimum 1-year follow-up. Overall, 85.3% of patients were satisfied. Older age generation and performing surgeon were predictors of satisfaction in both models. The ML model also retained cruciate-retaining/condylar-stabilizing implant; lack of inflammatory conditions, preoperative narcotic use, depression, and lumbar spine pain; female gender; and a preserved posterior cruciate ligament as predictors of satisfaction which allowed for a significantly higher area under the receiver operator characteristic curve compared to the binary logistic regression model (0.81 vs 0.60).ConclusionFindings indicate that patient satisfaction may be multifactorial with some factors beyond the scope of a surgeon’s control. Further study is warranted to investigate predictors of patient satisfaction particularly with awareness of differences in results between traditional statistical models and ML algorithms.Level of EvidenceTherapeutic Level III.     

Artificial Intelligence and Arthroplasty at a Single Institution: Real-World Applications of Machine Learning to Big Data,Value-Based Care,Mobile Health,and Remote Patient Monitoring

BackgroundDriven by the recent ubiquity of big data and computing power, we established the Machine Learning Arthroplasty Laboratory (MLAL) to examine and apply artificial intelligence (AI) to musculoskeletal medicine.MethodsIn this review, we discuss the 2 core objectives of the MLAL as they relate to the practice and progress of orthopedic surgery: (1) patient-specific, value-based care and (2) human movement.ResultsWe developed and validated several machine learning-based models for primary lower extremity arthroplasty that preoperatively predict patient-specific, risk-adjusted value metrics, including cost, length of stay, and discharge disposition, to provide improved expectation management, preoperative planning, and potential financial arbitration. Additionally, we leveraged passive, ubiquitous mobile technologies to build a small data registry of human movement surrounding TKA that permits remote patient monitoring to evaluate therapy compliance, outcomes, opioid intake, mobility, and joint range of motion.ConclusionThe rapid rate with which we in arthroplasty are acquiring and storing continuous data, whether passively or actively, demands an advanced processing approach: AI. By carefully studying AI techniques with the MLAL, we have applied this evolving technique as a first step that may directly improve patient outcomes and practice of orthopedics.     

人工智能在眼科疾病诊疗的应用现状

近年来，人工智能（artificial intelligence，AI）在医学领域的应用范围不断扩大，已成为医疗行业关注的焦点，在心血管疾病、乳腺癌、皮肤癌、糖尿病视网膜病变、白内障、青光眼及早产儿视网膜病变等诊疗中均有新进展。本综述旨在了解AI在眼科领域的应用现状、优势、进展及不足，为AI在眼科领域的进一步应用及推广提供更多信息。     

VMware医用虚拟桌面优化及性能监控策略

随着桌面虚拟化应用的日趋广泛，对桌面的性能和安全性的考虑也变得非常重要。本文根据虚拟桌面实际使用环境，提出并实施了虚拟桌面模板优化以及监测桌面的性能和安全防护策略，有效提高了桌面的连接效率和运行的稳定性。     

基于虚拟化环境的高可靠性PACS建设

本文介绍了江阴市人民医院基于虚拟化环境下的高可靠性图片存档及通信系统（PACS）的虚拟化配置和系统结构设计与实现过程，分析了虚拟化在医疗信息系统应用中的优势和不足，并为虚拟环境下PACS的建设提出了自己的观点。