AIMC Topic: Retrospective Studies

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Visceral Fat Quantified by a Fully Automated Deep-Learning Algorithm and Risk of Incident and Recurrent Diverticulitis.

Diseases of the colon and rectum
BACKGROUND: Obesity is a risk factor for diverticulitis. However, it remains unclear whether visceral fat area, a more precise measurement of abdominal fat, is associated with the risk of diverticulitis.

Prediction model of ipsilateral level II lymph node metastasis in papillary thyroid carcinoma.

Auris, nasus, larynx
OBJECTIVES: This study aimed to develop a predictive model for ipsilateral level II lymph node metastasis (LNM) in patients with papillary thyroid carcinoma (PTC) using machine learning techniques. The necessity of level II dissection in lateral neck...

AI-supported approaches for mammography single and double reading: A controlled multireader study.

European journal of radiology
PURPOSE: To assess the impact of artificial intelligence (AI) on the diagnostic performance of radiologists with varying experience levels in mammography reading, considering single and simulated double reading approaches.

An interpretable artificial intelligence approach to differentiate between blastocysts with similar or same morphological grades.

Human reproduction (Oxford, England)
STUDY QUESTION: Can a quantitative method be developed to differentiate between blastocysts with similar or same inner cell mass (ICM) and trophectoderm (TE) grades, while also reflecting their potential for live birth?

Estimating individualized effectiveness of receiving successful recanalization for ischemic stroke cases using machine learning techniques.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: Directly measuring the causal effect of mechanical thrombectomy (MT) for each ischemic stroke patient remains challenging, as it is impossible to observe the outcomes for both with and without successful recanalization in the same individ...

Development and validation of a predictive machine learning model for postoperative long-term diabetes insipidus following transsphenoidal surgery for sellar lesions.

Clinical neurology and neurosurgery
OBJECTIVE: Diabetes Insipidus (DI) is a common complication that occurs following transsphenoidal surgery for sellar lesions. DI is usually transient but can be permanent in select patients. Prior studies have described preoperative risk factors for ...

Deep learning-driven multi-class classification of brain strokes using computed tomography: A step towards enhanced diagnostic precision.

European journal of radiology
OBJECTIVE: To develop and validate deep learning models leveraging CT imaging for the prediction and classification of brain stroke conditions, with the potential to enhance accuracy and support clinical decision-making.

Keeping AI on Track: Regular monitoring of algorithmic updates in mammography.

European journal of radiology
PURPOSE: To demonstrate a method of benchmarking the performance of two consecutive software releases of the same commercial artificial intelligence (AI) product to trained human readers using the Personal Performance in Mammographic Screening scheme...