AIMC Topic: Adult

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Deep learning automatic segmentation and radiomics model for diagnosing pancreatic solid neoplasms in MRI.

BMC cancer
BACKGROUND: To develop and validate a deep learning tool for the automatic segmentation of pancreatic solid neoplasms and to establish a radiomics model for diagnosing these solid neoplasms in MRI.

Machine learning model based on preoperative MRI and clinical data for predicting pancreatic fistula after pancreaticoduodenectomy.

BMC medical imaging
OBJECTIVE: To establish and validate a machine learning model using preoperative multi-sequence MRI radiomic features and clinical data to predict pancreatic fistula after pancreaticoduodenectomy (PD).

Machine learning models for the prediction of COVID-19 prognosis in the primary health care setting.

BMC primary care
BACKGROUND: Establishing risk factors associated with severity and prognosis in the early stages of the disease is important to identify patients who need specialized care. Creating new clinical tools to improve health decisions and outcomes in the p...

Evaluation of anthropometric and ultrasonographic measurements with different machine learning methods in predicting difficult intubation: a prospective observational study.

BMC anesthesiology
INTRODUCTION: Difficult intubation is one of the most challenging scenarios to deal with due to increased morbidity and mortality. Machine learning systems can help predict this process in advance. This study aimed to predict whether patients had dif...

AI-generated images of familiar faces are indistinguishable from real photographs.

Cognitive research: principles and implications
Human users are now able to generate synthetic face images with artificial intelligence (AI) tools. Although indistinguishable from real photographs, these images have tended to feature fictional identities that do not exist in the real world. As a r...

Determinants of student adoption of artificial intelligence applications in higher education.

Scientific reports
The integration of artificial intelligence (AI) into educational settings has the potential to transform learning experiences; however, its adoption among students remains impacted by various factors. The current study assesses the determinant factor...

Resting state EEG reveals no reliable biomarkers of tinnitus laterality.

Scientific reports
This study assessed whether resting-state quantitative EEG (qEEG) can differentiate tinnitus laterality under rigorous multiple-comparison control and nested, cross-validated machine learning (ML). We analyzed 210 pre-specified qEEG features-spectral...

Prediction of preterm birth from cervical length measurements in twin pregnancies using machine learning.

Scientific reports
Multiple Cervical Length (CL) measurements are typically acquired throughout the course of twin pregnancy to detect the early stages of labour and identify pregnancies at a high risk of preterm delivery. This study uses Machine-Learning (ML) approach...

Integrating traditional Chinese pulse diagnosis with machine learning: novel approaches for pregnancy and coronary heart disease identification.

Scientific reports
This study integrated ancient Traditional Chinese Medicine (TCM) pulse diagnosis techniques with modern machine learning to advance contemporary medical diagnostics. A portable intelligent TCM pulse diagnostic device was developed using MEMS and CMOS...