AIMC Topic: Prognosis

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Machine learning based on SEER database to predict distant metastasis of thyroid cancer.

Endocrine
OBJECTIVE: Distant metastasis of thyroid cancer often indicates poor prognosis, and it is important to identify patients who have developed distant metastasis or are at high risk as early as possible. This paper aimed to predict distant metastasis of...

Cuproptosis gene-related, neural network-based prognosis prediction and drug-target prediction for KIRC.

Cancer medicine
BACKGROUND: Kidney renal clear cell carcinoma (KIRC), as a common case in renal cell carcinoma (RCC), has the risk of postoperative recurrence, thus its prognosis is poor and its prognostic markers are usually based on imaging methods, which have the...

CT-based deep learning model for predicting hospital discharge outcome in spontaneous intracerebral hemorrhage.

European radiology
OBJECTIVES: To predict the functional outcome of patients with intracerebral hemorrhage (ICH) using deep learning models based on computed tomography (CT) images.

Artificial intelligence universal biomarker prediction tool.

Journal of thrombosis and thrombolysis
Through experiencing cardiopulmonary arrest, an artificial intelligence universal biomarker prediction tool was developed to help patients understand improvement in the trends of their disease. PyPI tool handles two biomarkers, hbA1c for diabetes and...

Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs.

European radiology
OBJECTIVES: To develop and validate a deep learning-based prognostic model in patients with idiopathic pulmonary fibrosis (IPF) using chest radiographs.

Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models.

World journal of emergency surgery : WJES
BACKGROUND: To assess the efficacy of artificial intelligence (AI) models in diagnosing and prognosticating acute appendicitis (AA) in adult patients compared to traditional methods. AA is a common cause of emergency department visits and abdominal s...

Value of CT quantification in progressive fibrosing interstitial lung disease: a deep learning approach.

European radiology
OBJECTIVES: To evaluate the relationship of changes in the deep learning-based CT quantification of interstitial lung disease (ILD) with changes in forced vital capacity (FVC) and visual assessments of ILD progression, and to investigate their progno...

Predictive value of preoperative Fried Frailty Phenotype assessment and serum biomarkers on the prognosis of elderly patients with femoral neck fracture under general anesthesia within 3 months after surgery.

Turkish journal of medical sciences
BACKGROUND/AIM: Femoral neck fracture (FNF) seriously impact the health of the elderly and affect their long-term quality of life of the patients. This study aimed to determine whether combining the preoperative Fried Frailty Phenotype (FFP) with ser...

Machine learning application in autoimmune diseases: State of art and future prospectives.

Autoimmunity reviews
Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is...

Accuracy of artificial intelligence CT quantification in predicting COVID-19 subjects' prognosis.

PloS one
BACKGROUND: Artificial intelligence (AI)-aided analysis of chest CT expedites the quantification of abnormalities and may facilitate the diagnosis and assessment of the prognosis of subjects with COVID-19.