AIMC Topic: Retrospective Studies

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Clinical implementation and evaluation of deep learning-assisted automatic radiotherapy treatment planning for lung cancer.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: The purpose of the study is to investigate the clinical application of deep learning (DL)-assisted automatic radiotherapy planning for lung cancer.

Enhancing Outcome Prediction in Intracerebral Hemorrhage Through Deep Learning: A Retrospective Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: This study aimed to employ deep learning techniques to analyze and validate an automatic prognostic biomarker for predicting outcomes following intracerebral hemorrhage (ICH).

Interpretation of acid-base metabolism on arterial blood gas samples via machine learning algorithms.

Irish journal of medical science
BACKGROUND: Arterial blood gas evaluation is crucial for critically ill patients, as it provides essential information about acid-base metabolism and respiratory balance, but evaluation can be complex and time-consuming. Artificial intelligence can p...

The Role of Artificial Intelligence in Predicting Optic Neuritis Subtypes From Ocular Fundus Photographs.

Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society
BACKGROUND: Optic neuritis (ON) is a complex clinical syndrome that has diverse etiologies and treatments based on its subtypes. Notably, ON associated with multiple sclerosis (MS ON) has a good prognosis for recovery irrespective of treatment, where...

Machine Learning Prediction of Autism Spectrum Disorder From a Minimal Set of Medical and Background Information.

JAMA network open
IMPORTANCE: Early identification of the likelihood of autism spectrum disorder (ASD) using minimal information is crucial for early diagnosis and intervention, which can affect developmental outcomes.

A machine learning technology for addressing medication-related risk in older, multimorbid patients.

The American journal of managed care
OBJECTIVES: To evaluate the FeelBetter machine learning system's ability to accurately identify older patients with multimorbidity at Brigham and Women's Hospital at highest risk of medication-associated emergency department (ED) visits and hospitali...

Artificial Intelligence Machine Learning Algorithms Versus Standard Linear Demographic Analysis in Predicting Implant Size of Anatomic and Reverse Total Shoulder Arthroplasty.

Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews
BACKGROUND: Accurate and precise templating is paramount for anatomic total shoulder arthroplasty (TSA) and reverse total shoulder arthroplasty (RSA) to enhance preoperative planning, streamline surgery, and improve implant positioning. We aimed to e...

Integrating MRI-based radiomics and clinicopathological features for preoperative prognostication of early-stage cervical adenocarcinoma patients: in comparison to deep learning approach.

Cancer imaging : the official publication of the International Cancer Imaging Society
OBJECTIVES: The roles of magnetic resonance imaging (MRI) -based radiomics approach and deep learning approach in cervical adenocarcinoma (AC) have not been explored. Herein, we aim to develop prognosis-predictive models based on MRI-radiomics and cl...

Deep learning-based automated bone age estimation for Saudi patients on hand radiograph images: a retrospective study.

BMC medical imaging
PURPOSE: In pediatric medicine, precise estimation of bone age is essential for skeletal maturity evaluation, growth disorder diagnosis, and therapeutic intervention planning. Conventional techniques for determining bone age depend on radiologists' s...

Early predictive values of clinical assessments for ARDS mortality: a machine-learning approach.

Scientific reports
Acute respiratory distress syndrome (ARDS) is a devastating critical care syndrome with significant morbidity and mortality. The objective of this study was to evaluate the predictive values of dynamic clinical indices by developing machine-learning ...