AIMC Topic: Retrospective Studies
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Early and noninvasive prediction of response to neoadjuvant therapy for breast cancer via longitudinal ultrasound and MR deep learning: A multicentre study.
Academic radiology
Nov 13, 2024
RATIONALE AND OBJECTIVES: The early prediction of response to neoadjuvant chemotherapy (NAC) will aid in the development of personalized treatments for patients with breast cancer. This study investigated the value of longitudinal multimodal deep lea...
SPE-YOLO: A deep learning model focusing on small pulmonary embolism detection.
Computers in biology and medicine
Nov 13, 2024
OBJECTIVES: By developing the deep learning model SPE-YOLO, the detection of small pulmonary embolism has been improved, leading to more accurate identification of this condition. This advancement aims to better serve medical diagnosis and treatment.
The Value of Machine Learning Models in Predicting Factors Associated with the Need for Permanent Shunting in Patients with Intracerebral Hemorrhage Requiring Emergency Cerebrospinal Fluid Diversion.
World neurosurgery
Nov 13, 2024
OBJECTIVE: To assess the efficacy of machine learning models in identifying factors associated with the need for permanent ventricular shunt placement in patients experiencing intracerebral hemorrhage (ICH) who require emergency cerebrospinal fluid (...
Development of a deep learning algorithm for Paneth cell density quantification for inflammatory bowel disease.
EBioMedicine
Nov 13, 2024
BACKGROUND: Alterations in ileal Paneth cell (PC) density have been described in gut inflammatory diseases such as Crohn's disease (CD) and could be used as a biomarker for disease prognosis. However, quantifying PCs is time-intensive, a barrier for ...
Deep learning-based automated measurement of hip key angles and auxiliary diagnosis of developmental dysplasia of the hip.
BMC musculoskeletal disorders
Nov 13, 2024
OBJECTIVES: Anteroposterior pelvic radiographs remains the most widely employed method for diagnosing developmental dysplasia of the hip. This study aims to evaluate the accuracy of an artificial intelligence model in measuring angles in pelvic radio...
Predicting post-hepatectomy liver failure in patients with hepatocellular carcinoma: nomograms based on deep learning analysis of gadoxetic acid-enhanced MRI.
European radiology
Nov 12, 2024
OBJECTIVES: This study aimed to develop nomograms for predicting post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC), using deep learning analysis of Gadoxetic acid-enhanced hepatobiliary (HBP) MRI.
Predicting craniofacial fibrous dysplasia growth status: an exploratory study of a hybrid radiomics and deep learning model based on computed tomography images.
Oral surgery, oral medicine, oral pathology and oral radiology
Nov 12, 2024
OBJECTIVE: This study aimed to develop 3 models based on computed tomography (CT) images of patients with craniofacial fibrous dysplasia (CFD): a radiomics model (Model Rad), a deep learning (DL) model (Model DL), and a hybrid radiomics and DL model ...
Accuracy of 7 Artificial Intelligence-Based Intraocular Lens Power Calculation Formulas in Extremely Long Caucasian Eyes.
American journal of ophthalmology
Nov 12, 2024
PURPOSE: To compare 7 artificial intelligence (AI)-based intraocular lens (IOL) power calculation formulas in extremely long eyes.
Lens Implantation, Intraocular
Myopia, Degenerative
Middle Aged
Refraction, Ocular
Lenses, Intraocular
Visual Acuity
Biometry
Optics and Photonics
Axial Length, Eye
White People
Phacoemulsification
Reproducibility of Results
Aged
Aged, 80 and over
Male
Humans
Artificial Intelligence
Retrospective Studies
Female
Development of a Deep Learning Model for Classification of Hepatic Steatosis from Clinical Standard Ultrasound.
Ultrasound in medicine & biology
Nov 12, 2024
OBJECTIVE: Early detection and monitoring of hepatic steatosis can help establish appropriate preventative measures against progression to more advanced disease. We aimed to develop a deep learning (DL) program for classification of hepatic steatosis...
A Novel Machine Learning-based Predictive Model of Clinically Significant Prostate Cancer and Online Risk Calculator.
Urology
Nov 12, 2024
OBJECTIVE: To create a machine-learning predictive model combining prostate imaging-reporting and data system (PI-RADS) score, PSA density, and clinical variables to predict clinically significant prostate cancer (csPCa).
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