AIMC Topic: Humans

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Current imaging applications, radiomics, and machine learning modalities of CNS demyelinating disorders and its mimickers.

Journal of neurology
Distinguishing among neuroinflammatory demyelinating diseases of the central nervous system can present a significant diagnostic challenge due to substantial overlap in clinical presentations and imaging features. Collaboration between specialists, n...

Domain Knowledge Inclusive Monotonic Neural Network Guides Patient-Specific Induction of General Anesthesia Dosing.

A&A practice
BACKGROUND: Postinduction hypotension is a well-known risk factor for adverse postoperative outcomes. Anesthesiologists estimate anesthetic dosages based on a patient's chart and domain knowledge. Machine learning is increasingly applied in predictin...

Applications of artificial intelligence and urban innovation performance: A quasi-natural experiment based on the pilot zones for the innovative application of artificial intelligence.

PloS one
Innovation is the core driving force behind the ability to cope with risks and enhance urban competitiveness. Against the backdrop of frequent global crises and intensified competition, improving urban innovation performance is important. This study ...

Screening for Parkinson's disease using "computer vision".

PloS one
BACKGROUND: Identifying bradykinesia is crucial for diagnosing Parkinson's disease (PD). Traditionally, the finger-tapping test has been used, relying on subjective assessments by physicians. Computer vision offers a non-contact and cost-effective al...

Current trends and perspectives on the roles of multi-analytical methodologies in spirit authentication: Perspectives, challenges, and opportunities.

Food chemistry
The persistent occurrence of economically motivated food frauds and adulterations, causing substantial economic losses to enterprises and endangering consumers interests, critically impedes the sustainable development of the food industry. Spirits, c...

Machine learning-assisted triple-emission Ln-MOFs sensor array for detection of multiple PFCs in aqueous environments.

Biosensors & bioelectronics
Perfluorinated compounds (PFCs) are persistent environmental pollutants with potential carcinogenicity, posing a major threat to ecosystems and human health. Rapid identification of PFCs in complex environmental matrices remains challenging due to th...

TG-Mamba: Leveraging text guidance for predicting tumor mutation burden in lung cancer.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Tumor mutation burden (TMB) is a crucial biomarker for predicting the response of lung cancer patients to immunotherapy. Traditionally, TMB is quantified through whole-exome sequencing (WES), but the high costs and time requirements of WES limit its ...

C-net: Cross-organ cross-modality cswin-transformer coupled convolutional network for dual task transfer learning in lymph node segmentation and classification.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Deep learning has made notable strides in the ultrasonic diagnosis of lymph nodes, yet it faces three primary challenges: a limited number of lymph node images and a scarcity of annotated data; difficulty in comprehensively learning both local and gl...

Multimodal data fusion with irregular PSA kinetics for automated prostate cancer grading.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Prostate cancer (PCa) detection and accurate grading remain critical challenges in medical diagnostics. While deep learning has shown promise in medical image analysis, existing computer-aided diagnosis approaches primarily focus on image recognition...

LR-COBRAS: A logic reasoning-driven interactive medical image data annotation algorithm.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The volume of image data generated in the medical field is continuously increasing. Manual annotation is both costly and prone to human error. Additionally, deep learning-based medical image algorithms rely on large, accurately annotated training dat...