AIMC Topic: Humans

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DDLA: a double deep latent autoencoder for diabetic retinopathy diagnose based on continuous glucose sensors.

Medical & biological engineering & computing
The current diagnosis of diabetic retinopathy is based on fundus images and clinical experience. However, considering the ineffectiveness and non-portability of medical devices, we aimed to develop a diagnostic model for diabetic retinopathy based on...

Diagnosis of neuropsychiatric systemic lupus erythematosus by label-free serum microsphere-coupled SERS fingerprints with machine learning.

Biosensors & bioelectronics
Surface-enhanced Raman spectroscopy (SERS) is a powerful optical technique for non-invasive and label-free bioanalysis of liquid biopsy, facilitating to diagnosis of potential diseases. Neuropsychiatric systemic lupus erythematosus (NPSLE) is one of ...

Deep-learning denoising minimizes radiation exposure in neck CT beyond the limits of conventional reconstruction.

European journal of radiology
BACKGROUND: Neck computed tomography (NCT) is essential for diagnosing suspected neck tumors and abscesses, but radiation exposure can be an issue. In conventional reconstruction techniques, limiting radiation dose comes at the cost of diminished dia...

Deep learning ensembles for detecting brain metastases in longitudinal multi-modal MRI studies.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Metastatic brain cancer is a condition characterized by the migration of cancer cells to the brain from extracranial sites. Notably, metastatic brain tumors surpass primary brain tumors in prevalence by a significant factor, they exhibit an aggressiv...

An Ethically Supported Framework for Determining Patient Notification and Informed Consent Practices When Using Artificial Intelligence in Health Care.

Chest
Artificial intelligence (AI) is increasingly being used in health care. Without an ethically supportable, standard approach to knowing when patients should be informed about AI, hospital systems and clinicians run the risk of fostering mistrust among...

MicroRNA classification and discovery for major depressive disorder diagnosis: Towards a robust and interpretable machine learning approach.

Journal of affective disorders
BACKGROUND: Major depressive disorder (MDD) is notably underdiagnosed and undertreated due to its complex nature and subjective diagnostic methods. Biomarker identification would help provide a clearer understanding of MDD aetiology. Although machine...

Improved brain metastases segmentation using generative adversarial network and conditional random field optimization mask R-CNN.

Medical physics
BACKGROUND: In radiotherapy, the delineation of the gross tumor volume (GTV) in brain metastases using computed tomography (CT) simulation localization is very important. However, despite the criticality of this process, a pronounced gap exists in th...

Construction of an antidepressant priority list based on functional, environmental, and health risks using an interpretable mixup-transformer deep learning model.

Journal of hazardous materials
As emerging pollutants, antidepressants (AD) must be urgently investigated for risk identification and assessment. This study constructed a comprehensive-effect risk-priority screening system (ADRank) for ADs by characterizing AD functionality, occur...