Stem Cell Research

Multidimensional feature analysis shows stratification in robotic-motor-training gains based on the level of pre-training motor impairment in stroke.

Stroke involves heterogeneity in injury and ongoing endogenous recovery, which are seldom stratified before testing post-stroke robot assisted motor training (RAMT). Pretraining variations, especially sensory-motor differences may also affect the gains achieved from the RAMT. Moreover, one assessment test may not effectively characterize the baseline sensory-motor status or the RAMT gains. Pre-the...

Embryonic Quality Assessment using Advanced Deep Learning Architectures utilizing Microscopic Images of Blastocysts.

The accurate evaluation of embryonic quality which is a key aspect in Assisted Reproductive Technology (ART) and it is crucial to ensure the success of in vitro fertilization (IVF), especially at critical developmental phases like day 3 and day 5. To increase the success rate of implantation, morphological feature-based scoring techniques are currently the mainstay of embryo evaluation. Addressing...

Therapy for Abnormal Muscle Synergies in Stroke Using the ULIX Low-Impedance Robot.

Patients who suffer from stroke often experience synergistic movements that make completion of activities of daily living (ADL) difficult. Robotics is...

The Effects of Robotic Exoskeleton Gait Training on Improving Walking Adaptability in Persons with MS.

The goal of the present pilot investigation is to examine the effects of 8 weeks of supervised, over-ground gait training using a robotic exoskeleton ...

Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques.

Stem cell organoids are powerful models for studying organ development, disease modeling, drug screening, and regenerative medicine applications. The ...

Development and Validation of Fully Automatic Deep Learning-Based Algorithms for Immunohistochemistry Reporting of Invasive Breast Ductal Carcinoma

Immunohistochemistry (IHC) analysis is a well-accepted and widely used method for molecular subtyping, a procedure for prognosis and targeted therap...

Data Ethics in the Era of Healthcare Artificial Intelligence in Africa: An Ubuntu Philosophy Perspective

Data are essential in developing healthcare artificial intelligence (AI) systems. However, patient data collection, access, and use raise ethical co...

Machine learning in time-lapse imaging to differentiate embryos from young vs old mice†.

Time-lapse microscopy for embryos is a non-invasive technology used to characterize early embryo development. This study employs time-lapse microscopy...

Ethical considerations of use of hold-out sets in clinical prediction model management

Clinical prediction models are statistical or machine learning models used to quantify the risk of a certain health outcome using patient data. Thes...

Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models

The rapid progress in Large Language Models (LLMs) could transform many fields, but their fast development creates significant challenges for oversi...

Results From a Pilot Study of an Automated Directly Observed Therapy Intervention Using Artificial Intelligence With Conditional Economic Incentives Among Young Adults With HIV.

BACKGROUND: Despite improvements in antiretroviral therapy (ART) availability, suboptimal adherence is common among youth with HIV (YWH) and can incre...

Virtual Labs and Designer Bugs - Generative AI, Synthetic Biology and National Security.

AI technologies can pose a major national security concern. AI programs could be used to develop chemical and biological agents which circumvent exist...

Histopathology Based AI Model Predicts Anti-Angiogenic Therapy Response in Renal Cancer Clinical Trial

Predictive biomarkers of treatment response are lacking for metastatic clear cell renal cell carcinoma (ccRCC), a tumor type that is treated with an...

Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer.

Tumor molecular data sets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demons...

[The ethics of IA in medicine must be based on the practical ethics of the healthcare relationship].

THE ETHICS OF IA IN MEDICINE MUST BE BASED ON THE PRACTICAL ETHICS OF THE HEALTHCARE RELATIONSHIP. Artificial intelligence (AI) offers more and more a...

High-throughput genetic manipulation of multicellular organisms using a machine-vision guided embryonic microinjection robot.

Microinjection is a technique used for transgenesis, mutagenesis, cell labeling, cryopreservation, and in vitro fertilization in multiple single and m...

[ChatGPT: aid to medical ethics decision making?].

BACKGROUND: Physicians have to make countless decisions every day. The medical, ethical and legal aspects are often intertwined and subject to change ...

StructuralDPPIV: a novel deep learning model based on atom structure for predicting dipeptidyl peptidase-IV inhibitory peptides.

MOTIVATION: Diabetes is a chronic metabolic disorder that has been a major cause of blindness, kidney failure, heart attacks, stroke, and lower limb a...

Towards an Explainable AI Platform to Study Interruptions in Cancer Radiation Therapy.

Radiation therapy interruptions drive cancer treatment failures; they represent an untapped opportunity for improving outcomes and narrowing treatment...

Deep learning for automated segmentation in radiotherapy: a narrative review.

The segmentation of organs and structures is a critical component of radiation therapy planning, with manual segmentation being a laborious and time-c...