AIMC Topic: Humans

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Predictive utility of artificial intelligence on schizophrenia treatment outcomes: A systematic review and meta-analysis.

Neuroscience and biobehavioral reviews
Identifying optimal treatment approaches for schizophrenia is challenging due to varying symptomatology and treatment responses. Artificial intelligence (AI) shows promise in predicting outcomes, prompting this systematic review and meta-analysis to ...

Validation requirements for AI-based intervention-evaluation in aging and longevity research and practice.

Ageing research reviews
The field of aging and longevity research is overwhelmed by vast amounts of data, calling for the use of Artificial Intelligence (AI), including Large Language Models (LLMs), for the evaluation of geroprotective interventions. Such evaluations should...

Can people with epilepsy trust AI chatbots for information on physical exercise?

Epilepsy & behavior : E&B
PURPOSE: This study aims to evaluate the similarity, readability, and alignment with current scientific knowledge of responses from AI-based chatbots to common questions about epilepsy and physical exercise.

UDA-GS: A cross-center multimodal unsupervised domain adaptation framework for Glioma segmentation.

Computers in biology and medicine
Gliomas are the most common and malignant form of primary brain tumors. Accurate segmentation and measurement from MRI are crucial for diagnosis and treatment. Due to the infiltrative growth pattern of gliomas, their labeling is very difficult. In tu...

Deep learning-based overall survival prediction in patients with glioblastoma: An automatic end-to-end workflow using pre-resection basic structural multiparametric MRIs.

Computers in biology and medicine
PURPOSE: Accurate and automated early survival prediction is critical for patients with glioblastoma (GBM) as their poor prognosis requires timely treatment decision-making. To address this need, we developed a deep learning (DL)-based end-to-end wor...

Predicting cancer content in tiles of lung squamous cell carcinoma tumours with validation against pathologist labels.

Computers in biology and medicine
BACKGROUND: A growing body of research is using deep learning to explore the relationship between treatment biomarkers for lung cancer patients and cancer tissue morphology on digitized whole slide images (WSIs) of tumour resections. However, these W...

Self-supervised learning via VICReg enables training of EMG pattern recognition using continuous data with unclear labels.

Computers in biology and medicine
In this study, we investigate the application of self-supervised learning via pre-trained Long Short-Term Memory (LSTM) networks for training surface electromyography pattern recognition models (sEMG-PR) using dynamic data with transitions. While lab...

Going Smaller: Attention-based models for automated melanoma diagnosis.

Computers in biology and medicine
Computational approaches offer a valuable tool to aid with the early diagnosis of melanoma by increasing both the speed and accuracy of doctors' decisions. The latest and best-performing approaches often rely on large ensemble models, with the number...

Artificial intelligence for identification of candidates for device-aided therapy in Parkinson's disease: DELIST-PD study.

Computers in biology and medicine
INTRODUCTION: In Parkinson's Disease (PD), despite available treatments focusing on symptom alleviation, the effectiveness of conventional therapies decreases over time. This study aims to enhance the identification of candidates for device-aided the...

Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet.

Computers in biology and medicine
Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung cancers. Early and accurate diagnosis is essential for successful treatment, prompting the rise of ...