AIMC Topic: Retrospective Studies

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Deep Learning to Detect Pulmonary Hypertension from the Chest X-Ray Images of Patients with Systemic Sclerosis.

International heart journal
Pulmonary hypertension (PH) is a serious prognostic complication in patients with systemic sclerosis (SSc). Deep learning models can be applied to detect PH in the chest X-ray images of these patients. The aim of the study was to investigate the perf...

Prediction of 12-month recurrence of pancreatic cancer using machine learning and prognostic factors.

BMC medical informatics and decision making
BACKGROUND AND AIM: Pancreatic cancer is lethal and prevalent among other cancer types. The recurrence of this tumor is high, especially in patients who did not receive adjuvant therapies. Early prediction of PC recurrence has a significant role in e...

A new approach for sex prediction by evaluating mandibular arch and canine dimensions with machine-learning classifiers and intraoral scanners (a retrospective study).

Scientific reports
In circumstances where antemortem information concerning the deceased individual is unavailable, forensic experts prepare biological profiling for unidentified human remains that aids in narrowing the search for identity. Biological profiling include...

Predicting stroke severity of patients using interpretable machine learning algorithms.

European journal of medical research
BACKGROUND: Stroke is a significant global health concern, ranking as the second leading cause of death and placing a substantial financial burden on healthcare systems, particularly in low- and middle-income countries. Timely evaluation of stroke se...

Deep Learning Reconstruction for Enhanced Resolution and Image Quality in Breath-Hold MRCP: A Preliminary Study.

Journal of computer assisted tomography
OBJECTIVE: This preliminary study aims to assess the image quality of enhanced-resolution deep learning reconstruction (ER-DLR) in magnetic resonance cholangiopancreatography (MRCP) and compare it with non-ER-DLR MRCP images.

Early and noninvasive prediction of response to neoadjuvant therapy for breast cancer via longitudinal ultrasound and MR deep learning: A multicentre study.

Academic radiology
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
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
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
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
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...