AIMC Topic: Retrospective Studies

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Predicting the risk category of thymoma with machine learning-based computed tomography radiomics signatures and their between-imaging phase differences.

Scientific reports
The aim of this study was to develop a medical imaging and comprehensive stacked learning-based method for predicting high- and low-risk thymoma. A total of 126 patients with thymomas and 5 patients with thymic carcinoma treated at our institution, i...

Prediction of CD8+T lymphocyte infiltration levels in gastric cancer from contrast-enhanced CT and clinical factors using machine learning.

Medical physics
BACKGROUND: CD8+ T lymphocyte infiltration is closely associated with the prognosis and immunotherapy response of gastric cancer (GC). For now, the examination of CD8 infiltration levels relies on endoscopic biopsy, which is invasive and unsuitable f...

Predicting the Healing of Lower Extremity Fractures Using Wearable Ground Reaction Force Sensors and Machine Learning.

Sensors (Basel, Switzerland)
Lower extremity fractures pose challenges due to prolonged healing times and limited assessment methods. Integrating wearable sensors with machine learning can help overcome these challenges by providing objective assessment and predicting fracture h...

Beyond hand-crafted features for pretherapeutic molecular status identification of pediatric low-grade gliomas.

Scientific reports
The use of targeted agents in the treatment of pediatric low-grade gliomas (pLGGs) relies on the determination of molecular status. It has been shown that genetic alterations in pLGG can be identified non-invasively using MRI-based radiomic features ...

Using Artificial Intelligence for Assessment of Velopharyngeal Competence in Children Born With Cleft Palate With or Without Cleft Lip.

The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association
ObjectiveDevelopment of an AI tool to assess velopharyngeal competence (VPC) in children with cleft palate, with/without cleft lip.DesignInnovation of an AI tool using retrospective audio recordings and assessments of VPC.SettingTwo datasets were use...

Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty.

Journal of shoulder and elbow surgery
BACKGROUND: Reduced bone density is recognized as a predictor for potential complications in reverse shoulder arthroplasty (RSA). While humeral and glenoid planning based on preoperative computed tomography (CT) scans assist in implant selection and ...

Artificial intelligence-driven volumetric CT outcome score in cystic fibrosis: longitudinal and multicenter validation with/without modulators treatment.

European radiology
OBJECTIVES: Holistic segmentation of CT structural alterations with 3D deep learning has recently been described in cystic fibrosis (CF), allowing the measurement of normalized volumes of airway abnormalities (NOVAA-CT) as an automated quantitative o...

Predicting graft survival in paediatric kidney transplant recipients using machine learning.

Pediatric nephrology (Berlin, Germany)
BACKGROUND: Identification of factors that affect graft survival in kidney transplantation can increase graft survival and reduce mortality. Artificial intelligence modelling enables impartial evaluation of clinician bias. This study aimed to examine...

Advancing Emergency Department Triage Prediction With Machine Learning to Optimize Triage for Abdominal Pain Surgery Patients.

Surgical innovation
BACKGROUND: The development of emergency department (ED) triage systems remains challenging in accurately differentiating patients with acute abdominal pain (AAP) who are critical and urgent for surgery due to subjectivity and limitations. We use mac...