AIMC Topic: Retrospective Studies

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Automated Detection of Acute Myocardial Infarction Using Asynchronous Electrocardiogram Signals-Preview of Implementing Artificial Intelligence With Multichannel Electrocardiographs Obtained From Smartwatches: Retrospective Study.

Journal of medical Internet research
BACKGROUND: When using a smartwatch to obtain electrocardiogram (ECG) signals from multiple leads, the device has to be placed on different parts of the body sequentially. The ECG signals measured from different leads are asynchronous. Artificial int...

Technical details for a robot-assisted hand-sewn esophago-gastric anastomosis during minimally invasive Ivor Lewis esophagectomy.

Surgical endoscopy
BACKGROUND: Minimally invasive Ivor Lewis esophagectomy (MIILE) provides better outcomes than open techniques, particularly in terms of post-operative recovery and pulmonary complications. However, in addition to requiring advanced technical skills, ...

Automated machine learning for endemic active tuberculosis prediction from multiplex serological data.

Scientific reports
Serological diagnosis of active tuberculosis (TB) is enhanced by detection of multiple antibodies due to variable immune responses among patients. Clinical interpretation of these complex datasets requires development of suitable algorithms, a time c...

Deep learning detects acute myeloid leukemia and predicts NPM1 mutation status from bone marrow smears.

Leukemia
The evaluation of bone marrow morphology by experienced hematopathologists is essential in the diagnosis of acute myeloid leukemia (AML); however, it suffers from a lack of standardization and inter-observer variability. Deep learning (DL) can proces...

Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs.

Radiology
Background An artificial intelligence model that assesses primary bone tumors on radiographs may assist in the diagnostic workflow. Purpose To develop a multitask deep learning (DL) model for simultaneous bounding box placement, segmentation, and cla...

Stratifying the Risk of Cardiovascular Disease in Obstructive Sleep Apnea Using Machine Learning.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: Obstructive sleep apnea (OSA) is associated with higher risk of morbidity and mortality related to cardiovascular disease (CVD). Due to overlapping clinical risk factors, identifying high-risk patients with OSA who are likely t...

Multi-parametric MRI phenotype with trustworthy machine learning for differentiating CNS demyelinating diseases.

Journal of translational medicine
BACKGROUND: Misdiagnosis of multiple sclerosis (MS) and neuromyelitis optica (NMO) may delay the treatment, resulting in poor prognosis. However, the precise identification of these two diseases is still challenging in clinical practice. We aimed to ...

Applying interpretable deep learning models to identify chronic cough patients using EHR data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Chronic cough (CC) affects approximately 10% of adults. Many disease states are associated with chronic cough, such as asthma, upper airway cough syndrome, bronchitis, and gastroesophageal reflux disease. The lack of an ICD ...

Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network.

BMC medical imaging
BACKGROUND: We aimed to construct an artificial intelligence (AI) guided identification of suspicious bone metastatic lesions from the whole-body bone scintigraphy (WBS) images by convolutional neural networks (CNNs).

Deep Learning-Enabled Identification of Autoimmune Encephalitis on 3D Multi-Sequence MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Autoimmune encephalitis (AE) is a noninfectious emergency with severe clinical attacks. It is difficult for the earlier diagnosis of acute AE due to the lack of antibody detection resources.