AIMC Topic: Retrospective Studies
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Machine learning model-based preterm birth prediction and clinical nomogram: A big retrospective cohort study.
International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Nov 18, 2024
OBJECTIVE: This study sought to develop a multifactorial predictive model for preterm birth risk, with the goal of providing clinical practitioners with early prevention.
Deep learning based analysis of dynamic video ultrasonography for predicting cervical lymph node metastasis in papillary thyroid carcinoma.
Endocrine
Nov 18, 2024
BACKGROUND: Cervical lymph node metastasis (CLNM) is the most common form of thyroid cancer metastasis. Accurate preoperative CLNM diagnosis is of more importance in patients with papillary thyroid cancer (PTC). However, there is currently no unified...
Model Based on Ultrasound Radiomics and Machine Learning to Preoperative Differentiation of Follicular Thyroid Neoplasm.
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Nov 18, 2024
OBJECTIVES: To evaluate the value of radiomics based on ultrasonography in differentiating follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) and construct a tool for preoperative noninvasive predicting FTC and FTA.
A combined model integrating radiomics and deep learning based on multiparametric magnetic resonance imaging for classification of brain metastases.
Acta radiologica (Stockholm, Sweden : 1987)
Nov 18, 2024
BACKGROUND: Radiomics and deep learning (DL) can individually and efficiently identify the pathological type of brain metastases (BMs).
Deep learning-based automatic bleeding recognition during liver resection in laparoscopic hepatectomy.
Surgical endoscopy
Nov 18, 2024
BACKGROUND: Intraoperative hemorrhage during laparoscopic hepatectomy (LH) is a risk factor for negative postoperative outcomes. Ensuring appropriate hemostasis enhances the safety of surgical procedures. An automatic bleeding recognition system base...
Predictive accuracy of machine learning models for conservative treatment failure in thoracolumbar burst fractures.
BMC musculoskeletal disorders
Nov 18, 2024
BACKGROUND: The management of patients with thoracolumbar burst fractures remains a topic of debate, with conservative treatment being successful in most cases but not all. This study aimed to assess the utility of machine learning models (MLMs) in p...
Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer.
BMC cancer
Nov 18, 2024
BACKGROUND: Multiple first-line treatment options have been developed for advanced non-small cell lung cancer (NSCLC) in each subgroup determined by predictive biomarkers, specifically driver oncogene and programmed cell death ligand-1 (PD-L1) status...
Construction and SHAP interpretability analysis of a risk prediction model for feeding intolerance in preterm newborns based on machine learning.
BMC medical informatics and decision making
Nov 18, 2024
OBJECTIVE: To construct a highly accurate and interpretable feeding intolerance (FI) risk prediction model for preterm newborns based on machine learning (ML) to assist medical staff in clinical diagnosis.
Prediction of esophageal fistula in radiotherapy/chemoradiotherapy for patients with advanced esophageal cancer by a clinical-deep learning radiomics model : Prediction of esophageal fistula in radiotherapy/chemoradiotherapy patients.
BMC medical imaging
Nov 18, 2024
BACKGROUND: Esophageal fistula (EF), a rare and potentially fatal complication, can be better managed with predictive models for personalized treatment plans in esophageal cancers. We aim to develop a clinical-deep learning radiomics model for effect...
Technical feasibility of automated blur detection in digital mammography using convolutional neural network.
European radiology experimental
Nov 18, 2024
BACKGROUND: The presence of a blurred area, depending on its localization, in a mammogram can limit diagnostic accuracy. The goal of this study was to develop a model for automatic detection of blur in diagnostically relevant locations in digital mam...
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