AIMC Topic: Adult

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Prediction of gait recovery using machine learning algorithms in patients with spinal cord injury.

Medicine
With advances in artificial intelligence, machine learning (ML) has been widely applied to predict functional outcomes in clinical medicine. However, there has been no attempt to predict walking ability after spinal cord injury (SCI) based on ML. In ...

Do we empathize humanoid robots and humans in the same way? Behavioral and multimodal brain imaging investigations.

Cerebral cortex (New York, N.Y. : 1991)
Humanoid robots have been designed to look more and more like humans to meet social demands. How do people empathize humanoid robots who look the same as but are essentially different from humans? We addressed this issue by examining subjective feeli...

Enhancing Meibography Image Analysis Through Artificial Intelligence-Driven Quantification and Standardization for Dry Eye Research.

Translational vision science & technology
PURPOSE: This study enhances Meibomian gland (MG) infrared image analysis in dry eye (DE) research through artificial intelligence (AI). It is comprised of two main stages: automated eyelid detection and tarsal plate segmentation to standardize meibo...

Generative AI in glioma: Ensuring diversity in training image phenotypes to improve diagnostic performance for IDH mutation prediction.

Neuro-oncology
BACKGROUND: This study evaluated whether generative artificial intelligence (AI)-based augmentation (GAA) can provide diverse and realistic imaging phenotypes and improve deep learning-based classification of isocitrate dehydrogenase (IDH) type in gl...

An explainable machine learning model to solid adnexal masses diagnosis based on clinical data and qualitative ultrasound indicators.

Cancer medicine
BACKGROUND: Accurate characterization of newly diagnosed a solid adnexal lesion is a key step in defining the most appropriate therapeutic approach. Despite guidance from the International Ovarian Tumor Analyzes Panel, the evaluation of these lesions...

Arrhythmic Mitral Valve Prolapse Phenotype: An Unsupervised Machine Learning Analysis Using a Multicenter Cardiac MRI Registry.

Radiology. Cardiothoracic imaging
Purpose To use unsupervised machine learning to identify phenotypic clusters with increased risk of arrhythmic mitral valve prolapse (MVP). Materials and Methods This retrospective study included patients with MVP without hemodynamically significant ...

Characterization of unique pattern of immune cell profile in patients with nasopharyngeal carcinoma through flow cytometry and machine learning.

Journal of cellular and molecular medicine
In patients with nasopharyngeal carcinoma (NPC), the alteration of immune responses in peripheral blood remains unclear. In this study, we established an immune cell profile for patients with NPC and used flow cytometry and machine learning (ML) to i...

Recovering speech intelligibility with deep learning and multiple microphones in noisy-reverberant situations for people using cochlear implants.

The Journal of the Acoustical Society of America
For cochlear implant (CI) listeners, holding a conversation in noisy and reverberant environments is often challenging. Deep-learning algorithms can potentially mitigate these difficulties by enhancing speech in everyday listening environments. This ...

A novel deep learning framework for accurate melanoma diagnosis integrating imaging and genomic data for improved patient outcomes.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
BACKGROUND: Melanoma is one of the most malignant forms of skin cancer, with a high mortality rate in the advanced stages. Therefore, early and accurate detection of melanoma plays an important role in improving patients' prognosis. Biopsy is the tra...