The impact of extracorporeal membrane oxygenation on mortality in patients with cardiogenic shock post-acute myocardial infarction: a systematic review and meta-analysis.

Aims: Cardiogenic shock remains the leading cause of death in patients hospitalized with acute myocardial infarction. Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is increasingly used in the treatment of infarct-related cardiogenic shock. However, there is limited evidence regarding its beneficial impact on mortality. The aim of this study was to systematically review studies reporting the impact of VA-ECMO on mortality in patients with acute myocardial infarction complicated by cardiogenic shock. Methods and results: A comprehensive search of medical databases (Cochrane Register and PubMed) was conducted. Studies that reported mortality outcomes in patients treated with VA-ECMO for infarct-related cardiogenic shock were included. The database search yielded 1194 results, of which 11 studies were included in the systematic review. Four of these studies, with a total of 586 patients, were randomized controlled trials and were included in the meta-analysis. This demonstrated that there was no significant difference in 30-day all-cause mortality with the use of VA-ECMO compared with standard medical therapy [odds ratio (OR) 0.91; 95% confidence interval (CI) 0.65-1.27]. Meta-analysis of two studies showed that VA-ECMO was associated with a significant reduction in 12-month all-cause mortality (OR 0.31; 95% CI 0.11-0.86). Qualitative synthesis of the observational studies showed that age, serum creatinine, serum lactate, and successful revascularization are independent predictors of mortality. Conclusion: Veno-arterial extracorporeal membrane oxygenation does not improve 30-day all-cause mortality in patients with cardiogenic shock following acute myocardial infarction; however, there may be significant reduction in all-cause mortality at 12 months. Further studies are needed to delineate the potential benefit of VA-ECMO in long-term outcomes. Registration: The protocol was registered in the PROSPERO International Prospective Register of Systematic Reviews (ID: CRD42023461740).

Biosignal-based transferable attention Bi-ConvGRU deep network for hand-gesture recognition towards online upper-limb prosthesis control.

BACKGROUND AND OBJECTIVE: Upper-limb amputation can significantly affect a person's capabilities with a dramatic impact on their quality of life. As a biological signal, surface electromyogram (sEMG) provides a non-invasive means to measure underlying muscle activation patterns, corresponding to specific hand gestures. This project aims to develop a real-time deep learning based recognition model to automatically and reliably recognise these complex signals of a wide range of daily hand gestures from amputees and non-amputees. METHODS: This paper proposes an attention bidirectional Convolutional Gated Recurrent Unit (Bi-ConvGRU) deep neural network for hand-gesture recognition. By training on sEMG data from both amputees and non-amputees, the model can learn to recognise a group of fine-grained hand movements. This is a significantly more challenging and underexplored area, compared to existing studies on coarse-control in lower limbs. One dimensional CNNs are initially used to extract intra-channel features. The novel use of a bidirectional sequential GRU (Bi-GRU) deep neural network allows the exploration of correlation of muscle activation among multi-channel sEMG signals from both prior and posterior time sequences. Importantly, the attention mechanism is employed following Bi-GRU layers. This enables the model to learn vital parts and feature weights, increasing robustness to bio-data noise and irregularity. Finally, we introduce the first of its kind transfer learning, demonstrating that a baseline model pre-trained with non-amputee data can be effectively refined with amputee data to build a personalised model for amputees. RESULTS: The attention Bi-ConvGRU was evaluated on the benchmark database Ninapro, and achieved an average accuracy of 88.7%, outperforming the state-of-the-art on 18 gesture recognition by 6.7%. CONCLUSIONS: To our knowledge, the developed end-to-end deep learning model is the first of its kind that enables reliable predictive decision making in short time windows (160ms). This reduced latency limits physiological awareness, enabling the potential for real-time, online and thus more intuitive bio-control of prosthetic devices for amputees.

Inside the "black box": Embedding clinical knowledge in data-driven machine learning for heart disease diagnosis.

Background: Ischemic heart disease (IHD) caused by the narrowing of coronary arteries is a major cause of morbidity and mortality worldwide. Clinical diagnosis involves complex, costly, and potentially invasive procedures. Objective: To address this problem, we introduce a novel clinical knowledge-enhanced machine learning (ML) pipeline to assist in timely and cost-effective IHD prediction. Methods: Unlike conventional data-driven "black box" ML approaches, we propose an effective mechanism to engage clinical expertise and gain insight into the "black box" at each stage of model development, including data analysis, preprocessing, selecting the most clinically discriminative features, and model evaluation. One-hot feature encoding is introduced to expose hidden bias and highlight the important elements and features. Results: Experimental results on the benchmark Cleveland IHD dataset showed that the proposed clinical knowledge-enhanced ML pipeline overperformed state-of-the-art data-driven ML models, using even fewer features. Our model based on one-hot feature encoding and support vector machine achieved the best accuracy of 94.4% and sensitivity 95% by using only 7 discriminative attributes. Conclusion: We share insights and discuss the effectiveness of incorporating clinical input in machine learning to improve model performance, as well as addressing some practical issues such as data bias and interpretability. We hope this preliminary study on engaging clinical expertise to explore the "black box" would improve the trustworthiness of AI and its potential wider uptake in the medical field.

Applying machine learning to identify autistic adults using imitation: An exploratory study.

Autism spectrum condition (ASC) is primarily diagnosed by behavioural symptoms including social, sensory and motor aspects. Although stereotyped, repetitive motor movements are considered during diagnosis, quantitative measures that identify kinematic characteristics in the movement patterns of autistic individuals are poorly studied, preventing advances in understanding the aetiology of motor impairment, or whether a wider range of motor characteristics could be used for diagnosis. The aim of this study was to investigate whether data-driven machine learning based methods could be used to address some fundamental problems with regard to identifying discriminative test conditions and kinematic parameters to classify between ASC and neurotypical controls. Data was based on a previous task where 16 ASC participants and 14 age, IQ matched controls observed then imitated a series of hand movements. 40 kinematic parameters extracted from eight imitation conditions were analysed using machine learning based methods. Two optimal imitation conditions and nine most significant kinematic parameters were identified and compared with some standard attribute evaluators. To our knowledge, this is the first attempt to apply machine learning to kinematic movement parameters measured during imitation of hand movements to investigate the identification of ASC. Although based on a small sample, the work demonstrates the feasibility of applying machine learning methods to analyse high-dimensional data and suggest the potential of machine learning for identifying kinematic biomarkers that could contribute to the diagnostic classification of autism.