AIMC Topic: Retrospective Studies
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Machine learning approaches for predicting fetal macrosomia at different stages of pregnancy: a retrospective study in China.
BMC pregnancy and childbirth
Feb 11, 2025
BACKGROUND: Macrosomia presents significant risks to both maternal and neonatal health, however, accurate antenatal prediction remains a major challenge. This study aimed to develop machine learning approaches to enhance the prediction of fetal macro...
Predictive modeling of methadone poisoning outcomes in children ≤ 5 years: utilizing machine learning and the National Poison Data System for improved clinical decision-making.
European journal of pediatrics
Feb 11, 2025
UNLABELLED: The escalating therapeutic use of methadone has coincided with an increase in accidental ingestions, particularly among children ≤ 5 years. This study utilized machine learning (ML) methodologies on data from the National Poison Data Syst...
Prediction of High-risk Capsule Characteristics for Recurrence of Pleomorphic Adenoma in the Parotid Gland Based on Habitat Imaging and Peritumoral Radiomics: A Two-center Study.
Academic radiology
Feb 10, 2025
RATIONALE AND OBJECTIVES: This study aims to develop and validate an ultrasoundbased habitat imaging and peritumoral radiomics model for predicting high-risk capsule characteristics for recurrence of pleomorphic adenoma (PA) of the parotid gland whil...
Machine Learning Methods Based on Chest CT for Predicting the Risk of COVID-19-Associated Pulmonary Aspergillosis.
Academic radiology
Feb 10, 2025
RATIONALE AND OBJECTIVES: To develop and validate a machine learning model based on chest CT and clinical risk factors to predict secondary aspergillus infection in hospitalized COVID-19 patients.
Interpretable Machine Learning to Predict the Malignancy Risk of Follicular Thyroid Neoplasms in Extremely Unbalanced Data: Retrospective Cohort Study and Literature Review.
JMIR cancer
Feb 10, 2025
BACKGROUND: Diagnosing and managing follicular thyroid neoplasms (FTNs) remains a significant challenge, as the malignancy risk cannot be determined until after diagnostic surgery.
Interpretable machine learning-derived nomogram model for early detection of persistent diarrhea in Salmonella typhimurium enteritis: a propensity score matching based case-control study.
BMC infectious diseases
Feb 10, 2025
BACKGROUND: Salmonella typhimurium infection is a considerable global health concern, particularly in children, where it often leads to persistent diarrhea. This condition can result in severe health complications including malnutrition and cognitive...
Identifying invasiveness to aid lung adenocarcinoma diagnosis using deep learning and pathomics.
Scientific reports
Feb 10, 2025
Most classification efforts for primary subtypes of lung adenocarcinoma (LUAD) have not yet been integrated into clinical practice. This study explores the feasibility of combining deep learning and pathomics to identify tumor invasiveness in LUAD pa...
Ultrasonic-Based Radiomics Signature With Machine Learning for Differentiating Prognostic Subsets of Pediatric Peripheral Neuroblastic Tumors: A Retrospective Study.
Ultrasound in medicine & biology
Feb 8, 2025
OBJECTIVE: To construct and select a better model based on ultrasonic-based radiomics features and clinical characteristics for prognostic subsets of pediatric neuroblastic tumors.
Developing approaches to incorporate donor-lung computed tomography images into machine learning models to predict severe primary graft dysfunction after lung transplantation.
American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
Feb 7, 2025
Primary graft dysfunction (PGD) is a common complication after lung transplantation associated with poor outcomes. Although risk factors have been identified, the complex interactions between clinical variables affecting PGD risk are not well underst...
The implementation of knowledge-based planning with partial OAR contours for prostate radiotherapy.
Journal of applied clinical medical physics
Feb 7, 2025
PURPOSE: Intra- and inter-observer contour uncertainty is a continuous challenge in treatment planning for radiotherapy. Our proposed solution to address this challenge is the use of partial contours for treatment planning, focusing on uninvolved or ...
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