AIMC Topic: Retrospective Studies

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Artificial Intelligence in Minimally Invasive Adrenalectomy: Using Deep Learning to Identify the Left Adrenal Vein.

Surgical laparoscopy, endoscopy & percutaneous techniques
BACKGROUND: Minimally invasive adrenalectomy is the main surgical treatment option for the resection of adrenal masses. Recognition and ligation of adrenal veins are critical parts of adrenal surgery. The utilization of artificial intelligence and de...

A deep-learning radiomics-based lymph node metastasis predictive model for pancreatic cancer: a diagnostic study.

International journal of surgery (London, England)
OBJECTIVES: Preoperative lymph node (LN) status is essential in formulating the treatment strategy among pancreatic cancer patients. However, it is still challenging to evaluate the preoperative LN status precisely now.

Robotic Partial Radical Nephrectomy for Clinical T3a Tumors: A Narrative Review.

Journal of endourology
T3a renal masses include a diverse group of tumors that invade the perirenal and/or sinus fat, pelvicaliceal system, or renal vein. The majority of cT3a renal masses represent renal cell carcinoma (RCC) and have historically been treated with radica...

Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy.

Medicina (Kaunas, Lithuania)
: Multiple factors are associated with postoperative functional outcomes, such as acute kidney injury (AKI), following partial nephrectomy (PN). The pre-, peri-, and postoperative factors are heavily intertwined and change dynamically, making it diff...

An AI-Enabled Dynamic Risk Stratification for Emergency Department Patients with ECG and CXR Integration.

Journal of medical systems
Emergency department (ED) triage scale determines the priority of patient care and foretells the prognosis. However, the information retrieved from the initial assessment is limited, hindering the risk identification accuracy of triage. Therefore, we...

Exploring the challenge of early gastric cancer diagnostic AI system face in multiple centers and its potential solutions.

Journal of gastroenterology
BACKGROUND: Artificial intelligence (AI) performed variously among test sets with different diversity due to sample selection bias, which can be stumbling block for AI applications. We previously tested AI named ENDOANGEL, diagnosing early gastric ca...

A preoperative CT-based deep learning radiomics model in predicting the stage, size, grade and necrosis score and outcome in localized clear cell renal cell carcinoma: A multicenter study.

European journal of radiology
BACKGROUND AND PURPOSE: The Stage, Size, Grade and Necrosis (SSIGN) score is the most commonly used prognostic model in clear cell renal cell carcinoma (ccRCC) patients. It is a great challenge to preoperatively predict SSIGN score and outcome of ccR...

Improved pregnancy prediction performance in an updated deep-learning embryo selection model: a retrospective independent validation study.

Reproductive biomedicine online
RESEARCH QUESTION: What is the effect of increasing training data on the performance of ongoing pregnancy prediction after single vitrified-warmed blastocyst transfer (SVBT) in a deep-learning model?

Amplifying the Effects of Contrast Agents on Magnetic Resonance Images Using a Deep Learning Method Trained on Synthetic Data.

Investigative radiology
OBJECTIVES: Artificial intelligence (AI) methods can be applied to enhance contrast in diagnostic images beyond that attainable with the standard doses of contrast agents (CAs) normally used in the clinic, thus potentially increasing diagnostic power...