Artificial Neural Network for Total Laboratory Automation to Improve the Management of Sample Dilution

Short-Term (6 Weeks) Experience of a Modular Workcell for Hemostasis Testing Including an Intelligent Data Manager at a Tertiary Care Hospital

OBJECTIVE

Modular workcells could be a better solution than total laboratory automation (TLA) in hemostasis laboratories. Here, we evaluated the impact of implementing a modular workcell (HemoCell) with an intelligent data management facility (HemoHub).

METHODS

We compared the turnaround times (TATs), numbers of rerun samples, and rerun times pre- and postimplementation of the HemoCell at Gil Medical Center. Prothrombin time (PT), activated partial thromboplastin time (aPTT), D-dimer, and fibrinogen were evaluated.

RESULTS

The TAT standard deviations (SDs) and maximum TAT values decreased after HemoCell implementation, although the mean TATs for PT, aPTT, and D-dimer were increased. Numbers of rerun samples were increased (18.1/day vs 44.7/day). However, rerun times were reduced, and SDs were decreased during the post-HemoCell period compared with pre-HemoCell. Additionally, technologists needed smaller working space and less labor.

CONCLUSION

The modular workcell could improve quality and efficiency by providing more consistent TATs and shorter rerun times in the hemostasis laboratory.

Interplay between Artificial Intelligence and Biomechanics Modeling in the Cardiovascular Disease Prediction

Cardiovascular disease (CVD) is the most common cause of morbidity and mortality worldwide, and early accurate diagnosis is the key point for improving and optimizing the prognosis of CVD. Recent progress in artificial intelligence (AI), especially machine learning (ML) technology, makes it possible to predict CVD. In this review, we first briefly introduced the overview development of artificial intelligence. Then we summarized some ML applications in cardiovascular diseases, including ML-based models to directly predict CVD based on risk factors or medical imaging findings and the ML-based hemodynamics with vascular geometries, equations, and methods for indirect assessment of CVD. We also discussed case studies where ML could be used as the surrogate for computational fluid dynamics in data-driven models and physics-driven models. ML models could be a surrogate for computational fluid dynamics, accelerate the process of disease prediction, and reduce manual intervention. Lastly, we briefly summarized the research difficulties and prospected the future development of AI technology in cardiovascular diseases.

Automation and artificial intelligence in the clinical laboratory

The daily operation of clinical laboratories will be drastically impacted by two disruptive technologies: automation and artificial intelligence (the development and use of computer systems able to perform tasks that normally require human intelligence). These technologies will also expand the scope of laboratory medicine. Automation will result in increased efficiency but will require changes to laboratory infrastructure and a shift in workforce training requirements. The application of artificial intelligence to large clinical datasets generated through increased automation will lead to the development of new diagnostic and prognostic models. Together, automation and artificial intelligence will support the move to personalized medicine. Changes in pathology and clinical doctoral scientist training will be necessary to fully participate in these changes. KEYWORDS: Automation; artificial intelligence; deep learning; laboratory medicine.