AIMC Topic: Retrospective Studies

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A novel endoscopic artificial intelligence system to assist in the diagnosis of autoimmune gastritis: a multicenter study.

Endoscopy
BACKGROUND:  Autoimmune gastritis (AIG), distinct from Helicobacter pylori-associated atrophic gastritis (HpAG), is underdiagnosed due to limited awareness. This multicenter study aimed to develop a novel endoscopic artificial intelligence (AI) syste...

Clinical evaluation of accelerated diffusion-weighted imaging of rectal cancer using a denoising neural network.

European journal of radiology
BACKGROUND: To evaluate the effectiveness of a deep learning denoising approach to accelerate diffusion-weighted imaging (DWI) and thus improve diagnostic accuracy and image quality in restaging rectal MRI following total neoadjuvant therapy (TNT).

Using Atrial Fibrillation Burden Trends and Machine Learning to Predict Near-Term Risk of Cardiovascular Hospitalization.

Circulation. Arrhythmia and electrophysiology
BACKGROUND: Atrial fibrillation is associated with an increased risk of cardiovascular hospitalization (CVH), which may be triggered by changes in daily burden. Machine learning of dynamic trends in atrial fibrillation burden, as measured by insertab...

Predicting Treatment Outcomes in Patients with Drug-Resistant Tuberculosis and Human Immunodeficiency Virus Coinfection, Using Supervised Machine Learning Algorithm.

Pathogens (Basel, Switzerland)
Drug-resistant tuberculosis (DR-TB) and HIV coinfection present a conundrum to public health globally and the achievement of the global END TB strategy in 2035. A descriptive, retrospective review of medical records of patients, who were diagnosed wi...

Deep learning-based approach for acquisition time reduction in ventilation SPECT in patients after lung transplantation.

Radiological physics and technology
We aimed to evaluate the image quality and diagnostic performance of chronic lung allograft dysfunction (CLAD) with lung ventilation single-photon emission computed tomography (SPECT) images acquired briefly using a convolutional neural network (CNN)...

A deep-learning-based histopathology classifier for focal cortical dysplasia (FCD) unravels a complex scenario of comorbid FCD subtypes.

Epilepsia
OBJECTIVE: Recently, we developed a first artificial intelligence (AI)-based digital pathology classifier for focal cortical dysplasia (FCD) as defined by the ILAE classification. Herein, we tested the usefulness of the classifier in a retrospective ...

Multiparametric MRI-Based Deep Learning Models for Preoperative Prediction of Tumor Deposits in Rectal Cancer and Prognostic Outcome.

Academic radiology
RATIONALE AND OBJECTIVES: To investigate the predictive value of a deep learning model based on multiparametric MRI (mpMRI) for tumor deposit (TD) in rectal cancer (RC) patients and to analyze their prognosis.

Bloodstream Infections in Childhood Acute Myeloid Leukemia and Machine Learning Models: A Single-institutional Analysis.

Journal of pediatric hematology/oncology
Childhood acute myeloid leukemia (AML) requires intensive chemotherapy, which may result in life-threatening bloodstream infections (BSIs). This study evaluated whether machine learning (ML) could predict BSI using electronic medical records. All chi...

Machine learning models to further identify advantaged populations that can achieve functional cure of chronic hepatitis B virus infection after receiving Peg-IFN alpha treatment.

International journal of medical informatics
OBJECTIVE: Functional cure is currently the highest goal of hepatitis B virus(HBV) treatment.Pegylated interferon(Peg-IFN) alpha is an important drug for this purpose,but even in the hepatitis B e antigen(HBeAg)-negative population,there is still a p...

Deep-learning reconstruction enhances image quality of Adamkiewicz Artery in low-keV dual-energy CT.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Low-keV virtual monoenergetic images (VMIs) of dual-energy computed tomography (CT) enhances iodine contrast for detecting small arteries like the Adamkiewicz artery (AKA), but image noise can be problematic. Deep-learning image reconstru...