AIMC Topic: Humans

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Explainable machine-learning-based prediction of QCT/FEA-calculated femoral strength under stance loading configuration using radiomics features.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Finite element analysis can provide precise femoral strength assessment. However, its modeling procedures were complex and time-consuming. This study aimed to develop a model to evaluate femoral strength calculated by quantitative computed tomography...

Computerizing the first step of the two-step algorithm in dermoscopy: A convolutional neural network for differentiating melanocytic from non-melanocytic skin lesions.

European journal of cancer (Oxford, England : 1990)
IMPORTANCE: Convolutional neural networks (CNN) have shown performance equal to trained dermatologists in differentiating benign from malignant skin lesions. To improve clinicians' management decisions, additional classifications into diagnostic cate...

Accuracy of vestibular schwannoma segmentation using deep learning models - a systematic review & meta-analysis.

Neuroradiology
UNLABELLED: Vestibular Schwannoma (VS) is a rare tumor with varied incidence rates, predominantly affecting the 60-69 age group. In the era of artificial intelligence (AI), deep learning (DL) algorithms show promise in automating diagnosis. However, ...

Navigating the maze of mass spectra: a machine-learning guide to identifying diagnostic ions in O-glycan analysis.

Analytical and bioanalytical chemistry
Structural details of oligosaccharides, or glycans, often carry biological relevance, which is why they are typically elucidated using tandem mass spectrometry. Common approaches to distinguish isomers rely on diagnostic glycan fragments for annotati...

Multi-source fully test-time adaptation.

Neural networks : the official journal of the International Neural Network Society
Deep neural networks have significantly advanced various fields. However, these models often encounter difficulties in achieving effective generalization when the distribution of test samples varies from that of the training samples. Recently, some f...

Ultrasound-Based Deep Learning Radiomics Nomogram for Tumor and Axillary Lymph Node Status Prediction After Neoadjuvant Chemotherapy.

Academic radiology
RATIONALE AND OBJECTIVES: This study aims to explore the feasibility of the deep learning radiomics nomogram (DLRN) for predicting tumor status and axillary lymph node metastasis (ALNM) after neoadjuvant chemotherapy (NAC) in patients with breast can...

Should Doctor Robot possess moral empathy?

Bioethics
Critics of clinical artificial intelligence (AI) suggest that the technology is ethically harmful because it may lead to the dehumanization of the doctor-patient relationship (DPR) by eliminating moral empathy, which is viewed as a distinctively huma...

Generative commonsense knowledge subgraph retrieval for open-domain dialogue response generation.

Neural networks : the official journal of the International Neural Network Society
Grounding on a commonsense knowledge subgraph can help the model generate more informative and diverse dialogue responses. Prior Traverse-based works explicitly retrieve a subgraph from the external knowledge base (eKB). Notably, the available knowle...

Center of Pressure- and Machine Learning-based Gait Score and Clinical Risk Factors for Predicting Functional Outcome in Acute Ischemic Stroke.

Archives of physical medicine and rehabilitation
OBJECTIVES: To investigate whether machine learning (ML)-based center of pressure (COP) analysis for gait assessment, when used in conjunction with clinical information, offers additive benefits in predicting functional outcomes in patients with acut...

Metabolic signatures derived from whole-brain MR-spectroscopy identify early tumor progression in high-grade gliomas using machine learning.

Journal of neuro-oncology
PURPOSE: Recurrence for high-grade gliomas is inevitable despite maximal safe resection and adjuvant chemoradiation, and current imaging techniques fall short in predicting future progression. However, we introduce a novel whole-brain magnetic resona...