AIMC Topic: Retrospective Studies
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Development of a machine learning model and a web application for predicting neurological outcome at hospital discharge in spinal cord injury patients.
The spine journal : official journal of the North American Spine Society
Jan 31, 2025
BACKGROUND: Spinal cord injury (SCI) is a devastating condition with profound physical, psychological, and socioeconomic consequences. Despite advances in SCI treatment, accurately predicting functional recovery remains a significant challenge. Conve...
External validation of 12 existing survival prediction models for patients with spinal metastases.
The spine journal : official journal of the North American Spine Society
Jan 31, 2025
BACKGROUND CONTEXT: Survival prediction models for patients with spinal metastases may inform patients and clinicians in shared decision-making.
Integrating machine learning for treatment decisions in anterior open bite orthodontic cases: A retrospective study.
Journal of the World federation of orthodontists
Jan 31, 2025
INTRODUCTION: This article explores the integration of machine learning (ML) algorithms to aid in treatment planning and extraction decisions for anterior open bite cases, leveraging demographic, clinical, and radiographic data to predict treatment o...
A novel artificial intelligence framework to quantify the impact of clinical compared with nonclinical influences on postoperative length of stay.
Surgery
Jan 31, 2025
BACKGROUND: The relative proportion of clinical compared with nonclinical influences on length of stay after colectomy has never been measured. We developed a novel machine-learning framework that quantifies the proportion of length of stay after col...
TransAnaNet: Transformer-based anatomy change prediction network for head and neck cancer radiotherapy.
Medical physics
Jan 31, 2025
BACKGROUND: Adaptive radiotherapy (ART) can compensate for the dosimetric impact of anatomic change during radiotherapy of head-neck cancer (HNC) patients. However, implementing ART universally poses challenges in clinical workflow and resource alloc...
The potential use of deep learning in performing autocorrection of setup errors in patients receiving radiotherapy.
Radiography (London, England : 1995)
Jan 31, 2025
INTRODUCTION: Modern radiotherapy practice relies on multiple approaches for verification of patient positioning. All of these techniques require experienced radiotherapists who understand the anatomical landmarks and the limitations of the used veri...
Preoperative anemia is an unsuspecting driver of machine learning prediction of adverse outcomes after lumbar spinal fusion.
The spine journal : official journal of the North American Spine Society
Jan 30, 2025
BACKGROUND CONTEXT: Preoperative risk assessment remains a challenge in spinal fusion operations. Predictive modeling provides data-driven estimates of postsurgical outcomes, guiding clinical decisions and improving patient care. Moreover, automated ...
The impact of updated imaging software on the performance of machine learning models for breast cancer diagnosis: a multi-center, retrospective study.
Archives of gynecology and obstetrics
Jan 30, 2025
PURPOSE: Artificial Intelligence models based on medical (imaging) data are increasingly developed. However, the imaging software on which the original data is generated is frequently updated. The impact of updated imaging software on the performance...
Preoperative blood and CT-image nutritional indicators in short-term outcomes and machine learning survival framework of intrahepatic cholangiocarcinoma.
European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Jan 30, 2025
BACKGROUND&AIMS: Intrahepatic cholangiocarcinoma (iCCA) is aggressive with limited treatment and poor prognosis. Preoperative nutritional status assessment is crucial for predicting outcomes in patients. This study aimed to compare the predictive cap...
Prognosis
Retrospective Studies
Tomography, X-Ray Computed
Middle Aged
Bilirubin
Humans
Serum Albumin
Preoperative Period
Nutrition Assessment
Hepatectomy
Machine Learning
Cholangiocarcinoma
Intra-Abdominal Fat
Postoperative Complications
Bile Duct Neoplasms
Subcutaneous Fat
Neoplasm Recurrence, Local
Female
Nutritional Status
Aged
Survival Rate
Male
Super-resolution deep-learning reconstruction with 1024 matrix improves CT image quality for pancreatic ductal adenocarcinoma assessment.
European journal of radiology
Jan 30, 2025
OBJECTIVES: To evaluate the efficiency of super-resolution deep-learning reconstruction (SR-DLR) optimized for helical body imaging in assessing pancreatic ductal adenocarcinoma (PDAC) using normal-resolution (NR) CT scanner.
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