AIMC Topic: Retrospective Studies

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Deep learning-based vessel automatic recognition for laparoscopic right hemicolectomy.

Surgical endoscopy
BACKGROUND: In laparoscopic right hemicolectomy (RHC) for right-sided colon cancer, accurate recognition of the vascular anatomy is required for appropriate lymph node harvesting and safe operative procedures. We aimed to develop a deep learning mode...

Can the generalizability issue of artificial intelligence be overcome? Pneumothorax detection algorithm.

Journal of investigative medicine : the official publication of the American Federation for Clinical Research
The generalizability of artificial intelligence (AI) models is a major issue in the field of AI applications. Therefore, we aimed to overcome the generalizability problem of an AI model developed for a particular center for pneumothorax detection usi...

Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU.

Scientific reports
Fluid overload, while common in the ICU and associated with serious sequelae, is hard to predict and may be influenced by ICU medication use. Machine learning (ML) approaches may offer advantages over traditional regression techniques to predict it. ...

Outcome prediction of methadone poisoning in the United States: implications of machine learning in the National Poison Data System (NPDS).

Drug and chemical toxicology
Methadone is an opioid receptor agonist with a high potential for abuse. The current study aimed to compare different machine learning models to predict the outcomes following methadone poisoning. This six-year retrospective longitudinal study utiliz...

MRI Deep Learning-Based Automatic Segmentation of Interventricular Septum for Black-Blood Myocardial T2* Measurement in Thalassemia.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: The T2* value of interventricular septum is routinely reported for grading myocardial iron load in thalassemia major, and automatic segmentation of septum could shorten analysis time and reduce interobserver variability.

Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network.

European radiology
OBJECTIVES: Reliable detection of disease-specific atrophy in individual T1w-MRI by voxel-based morphometry (VBM) requires scanner-specific normal databases (NDB), which often are not available. The aim of this retrospective study was to design, trai...

Robot-assisted retroperitoneal lymph node dissection: Initial experience in Japan.

Asian journal of endoscopic surgery
For patients with testicular tumors who need the surgical management, open retroperitoneal lymph node dissection (O-RPLND) is considered the gold standard treatment. However, recently, robot-assisted RPLND (R-RPLND) has gained popularity as a minimal...

Localization of early infarction on non-contrast CT images in acute ischemic stroke with deep learning approach.

Scientific reports
Localization of early infarction on first-line Non-contrast computed tomogram (NCCT) guides prompt treatment to improve stroke outcome. Our previous study has shown a good performance in the identification of ischemic injury on NCCT. In the present s...

Automated deep learning-based AMD detection and staging in real-world OCT datasets (PINNACLE study report 5).

Scientific reports
Real-world retinal optical coherence tomography (OCT) scans are available in abundance in primary and secondary eye care centres. They contain a wealth of information to be analyzed in retrospective studies. The associated electronic health records a...

Pulmonary abnormality screening on chest x-rays from different machine specifications: a generalized AI-based image manipulation pipeline.

European radiology experimental
BACKGROUND: Chest x-ray is commonly used for pulmonary abnormality screening. However, since the image characteristics of x-rays highly depend on the machine specifications, an artificial intelligence (AI) model developed for specific equipment usual...