AIMC Topic: Humans

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MRI deep learning models for assisted diagnosis of knee pathologies: a systematic review.

European radiology
OBJECTIVES: Despite showing encouraging outcomes, the precision of deep learning (DL) models using different convolutional neural networks (CNNs) for diagnosis remains under investigation. This systematic review aims to summarise the status of DL MRI...

Automated AI-based coronary calcium scoring using retrospective CT data from SCAPIS is accurate and correlates with expert scoring.

European radiology
OBJECTIVES: Evaluation of the correlation and agreement between AI and semi-automatic evaluations of calcium scoring CT (CSCT) examinations using extensive data from the Swedish CardioPulmonary bio-Image study (SCAPIS).

Future of allergy and immunology: Is artificial intelligence the key in the digital era?

Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology
Artificial intelligence (AI) is reshaping allergy and immunology by integrating cutting-edge technology to enhance patient outcomes and redefine clinical practices and research. This review evaluates AI's evolving role, emphasizing its impact on diag...

Editorial Commentary: Biomedical Research Investigating Artificial Intelligence Large Language Models Needs to Move Beyond Measuring Accuracy and Focus on Improving Patient Care.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
Orthopaedic surgeons are fascinated with artificial intelligence (AI). Since the release of ChatGPT to the general public on November 30, 2022, there have been a flurry of articles regarding use of large language models (LLMs) in our field. Most of t...

Assessment of machine learning classifiers for predicting intraoperative blood transfusion in non-cardiac surgery.

Transfusion clinique et biologique : journal de la Societe francaise de transfusion sanguine
BACKGROUND: This study aimed to develop a machine learning classifier for predicting intraoperative blood transfusion in non-cardiac surgeries.

Machine learning-based identification and validation of immune-related biomarkers for early diagnosis and targeted therapy in diabetic retinopathy.

Gene
The early diagnosis of diabetic retinopathy (DR) is challenging, highlighting the urgent need to identify new biomarkers. Immune responses play a crucial role in DR, yet there are currently no reports of machine learning (ML) algorithms being utilize...

OSAIRIS: Lessons Learned From the Hospital-Based Implementation and Evaluation of an Open-Source Deep-Learning Model for Radiotherapy Image Segmentation.

Clinical oncology (Royal College of Radiologists (Great Britain))
Several studies report the benefits and accuracy of using autosegmentation for organ at risk (OAR) outlining in radiotherapy treatment planning. Typically, evaluations focus on accuracy metrics, and other parameters such as perceived utility and safe...

GO-MAE: Self-supervised pre-training via masked autoencoder for OCT image classification of gynecology.

Neural networks : the official journal of the International Neural Network Society
Genitourinary syndrome of menopause (GSM) is a physiological disorder caused by reduced levels of oestrogen in menopausal women. Gradually, its symptoms worsen with age and prolonged menopausal status, which gravely impacts the quality of life as wel...

Optimized deep learning networks for accurate identification of cancer cells in bone marrow.

Neural networks : the official journal of the International Neural Network Society
Radiologists utilize pictures from X-rays, magnetic resonance imaging, or computed tomography scans to diagnose bone cancer. Manual methods are labor-intensive and may need specialized knowledge. As a result, creating an automated process for disting...