AIMC Topic: Humans

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The puzzling Spitz tumours: is artificial intelligence the key to their understanding?

Histopathology
Since their first description in 1948, Spitz tumours remain one of the most challenging diagnostic entities in dermatopathology due to their complex histological features and ambiguous clinical behaviour. In recent years, artificial intelligence (AI)...

Plasma Cytokine and Chemokine Profiles Predict Efficacy and Toxicity of Anti-CD19 CAR-T Cell Therapy in Large B-Cell Lymphoma.

Clinical lymphoma, myeloma & leukemia
BACKGROUND: Anti-CD19 chimeric antigen receptor T-cell (CAR-T) therapy has emerged as a promising treatment for large B-cell lymphoma (LBCL); however, durable complete responses are achieved in only 30% to 40% of patients. Additionally, CAR-T therapy...

Diastolic Versus Systolic or Mean Intraoperative Hypotension as Predictive of Perioperative Myocardial Injury in a White-Box Machine-Learning Model.

Anesthesia and analgesia
BACKGROUND: Intraoperative hypotension (IOH) and tachycardia are associated with perioperative myocardial injury (PMI), and thereby increased postoperative mortality. Patients undergoing vascular surgery are specifically at risk of developing cardiac...

Artificial intelligence-powered coronary artery disease diagnosis from SPECT myocardial perfusion imaging: a comprehensive deep learning study.

European journal of nuclear medicine and molecular imaging
BACKGROUND: Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is a well-established modality for noninvasive diagnostic assessment of coronary artery disease (CAD). However, the time-consuming and experience-...

Artificial intelligence-based model to predict recurrence after local excision in T1 rectal cancer.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
BACKGROUND: According to current guideline, patients with resected specimens showing high-risk features are recommended additional surgery after local excision (LE) of T1 colorectal cancer, despite the low incidence of recurrence. However, surgical r...

Inertial primal-dual projection neurodynamic approaches for constrained convex optimization problems and application to sparse recovery.

Neural networks : the official journal of the International Neural Network Society
Second-order (inertial) neurodynamic approaches are excellent tools for solving convex optimization problems in an accelerated manner, while the majority of existing approaches to neurodynamic approaches focus on unconstrained and simple constrained ...

A Multi-objective transfer learning framework for time series forecasting with Concept Echo State Networks.

Neural networks : the official journal of the International Neural Network Society
This paper introduces a novel transfer learning framework for time series forecasting that uses Concept Echo State Network (CESN) and a multi-objective optimization strategy. Our approach addresses the challenges of feature extraction and knowledge t...

Open-world semi-supervised relation extraction.

Neural networks : the official journal of the International Neural Network Society
Semi-supervised Relation Extraction methods play an important role in extracting relationships from unstructured text, which can leverage both labeled and unlabeled data to improve extraction accuracy. However, these methods are grounded under the cl...

Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer.

Seminars in cancer biology
Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although ur...

System for Predicting Neurological Outcomes Following Cardiac Arrest Based on Clinical Predictors Using a Machine Learning Method: The Neurological Outcomes After Cardiac Arrest Method.

Neurocritical care
BACKGROUND: A multimodal approach may prove effective for predicting clinical outcomes following cardiac arrest (CA). We aimed to develop a practical predictive model that incorporates clinical factors related to CA and multiple prognostic tests usin...