AIMC Topic: Adult

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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...

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...

Machine learning for (non-)epileptic tissue detection from the intraoperative electrocorticogram.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Clinical visual intraoperative electrocorticography (ioECoG) reading intends to localize epileptic tissue and improve epilepsy surgery outcome. We aimed to understand whether machine learning (ML) could complement ioECoG reading, how subgr...

Estimation of human age using machine learning on panoramic radiographs for Brazilian patients.

Scientific reports
This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with advanced machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach to age estimation. Our study in...

Artificial Intelligence Prediction Model of Occurrence of Cerebral Vasospasms Based on Machine Learning.

Journal of neurological surgery. Part A, Central European neurosurgery
BACKGROUND:  Symptomatic cerebral vasospasms are deleterious complication of the rupture of a cerebral aneurysm and potentially lethal. The existing scales used to classify the initial presentation of a subarachnoid hemorrhage (SAH) offer a blink of ...

Artificial intelligence-driven automated lung sizing from chest radiographs.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
Lung size measurements play an important role in transplantation, as optimal donor-recipient size matching is necessary to ensure the best possible outcome. Although several strategies for size matching are currently used, all have limitations, and n...

Metadata information and fundus image fusion neural network for hyperuricemia classification in diabetes.

Computer methods and programs in biomedicine
OBJECTIVE: In diabetes mellitus patients, hyperuricemia may lead to the development of diabetic complications, including macrovascular and microvascular dysfunction. However, the level of blood uric acid in diabetic patients is obtained by sampling p...

Artificial intelligence-based model for the recurrence of hepatocellular carcinoma after liver transplantation.

Surgery
BACKGROUND: Artificial intelligence-based models might improve patient selection for liver transplantation in hepatocellular carcinoma. The objective of the current study was to develop artificial intelligence-based deep learning models and determine...

Deep Learning With Ultrasound Images Enhance the Diagnosis of Nonalcoholic Fatty Liver.

Ultrasound in medicine & biology
OBJECTIVE: This research aimed to improve diagnosis of non-alcoholic fatty liver disease (NAFLD) by deep learning with ultrasound Images and reduce the impact of the professional competence and personal bias of the diagnostician.