AIMC Topic: Middle Aged

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Deep Learning of Computed Tomography Virtual Wedge Resection for Prediction of Histologic Usual Interstitial Pneumonitis.

Annals of the American Thoracic Society
The computed tomography (CT) pattern of definite or probable usual interstitial pneumonia (UIP) can be diagnostic of idiopathic pulmonary fibrosis and may obviate the need for invasive surgical biopsy. Few machine-learning studies have investigated ...

Artificial Intelligence and Clinical Decision Making: The New Nature of Medical Uncertainty.

Academic medicine : journal of the Association of American Medical Colleges
Estimates in a 1989 study indicated that physicians in the United States were unable to reach a diagnosis that accounted for their patient's symptoms in up to 90% of outpatient patient encounters. Many proponents of artificial intelligence (AI) see t...

Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: Chest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image analysis tools. We aimed to identify associ...

A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images.

Current medical imaging
BACKGROUND: Scanning a patient's lungs to detect Coronavirus 2019 (COVID-19) may lead to similar imaging of other chest diseases. Thus, a multidisciplinary approach is strongly required to confirm the diagnosis. There are only a few works targeted at...

Detection of Asymptomatic Carotid Artery Stenosis in High-Risk Individuals of Stroke Using a Machine-Learning Algorithm.

Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih
Objective Asymptomatic carotid stenosis (ACS) is closely associated to the incidence of severe cerebrovascular diseases. Early identifying the individuals with ACS and its associated risk factors could be beneficial for primary prevention of stroke. ...

[Risk factors for multiple debridements of the patients with deep incisional surgical site infection after spinal surgery].

Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences
OBJECTIVE: To investigate the risk factors that contribute to multiple debridements in patients suffering from deep incisional surgical site infection after spinal surgery and advise medical personnel to pay special attention to these risk factors.

[A Case of Port-Site Herniation from an Eight mm Port after Robot-Assisted Distal Gastrectomy].

Gan to kagaku ryoho. Cancer & chemotherapy
A 63-year-old-woman was diagnosed with gastric cancer cStage ⅠA after ESD, and then, underwent robot-assisted distal gastrectomy. She vomited on the postoperative day 2 and then was inserted nasogastric tube. The amount of drainage from the tube was ...

New perspectives in the prediction of postoperative complications for high-risk ulcerative colitis patients: machine learning preliminary approach.

European review for medical and pharmacological sciences
OBJECTIVE: Patients with acute severe and medical refractory ulcerative colitis have a high risk of postoperative complications after total abdominal colectomy (TAC). The objective of this retrospective study is to use machine learning to analyze and...

Predicting Secukinumab Fast-Responder Profile in Psoriatic Patients: Advanced Application of Artificial-Neural-Networks (ANNs).

Journal of drugs in dermatology : JDD
BACKGROUND: Drug resistance to biologics in psoriasis therapy can occur – it may be acquired during a treatment or else present itself from the beginning. To date, no biomarkers are known that may reliably guide clinicians in predicting respons...

Development of a machine learning algorithm for early detection of opioid use disorder.

Pharmacology research & perspectives
BACKGROUND: Opioid use disorder (OUD) affects an estimated 16 million people worldwide. The diagnosis of OUD is commonly delayed or missed altogether. We aimed to test the utility of machine learning in creating a prediction model and algorithm for e...