AIMC Topic: Retrospective Studies

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Characterization of an artificial intelligence model for ranking static images of blastocyst stage embryos.

Fertility and sterility
OBJECTIVE: To perform a series of analyses characterizing an artificial intelligence (AI) model for ranking blastocyst-stage embryos. The primary objective was to evaluate the benefit of the model for predicting clinical pregnancy, whereas the second...

Deep Learning Using Multiple Degrees of Maximum-Intensity Projection for PET/CT Image Classification in Breast Cancer.

Tomography (Ann Arbor, Mich.)
Deep learning (DL) has become a remarkably powerful tool for image processing recently. However, the usefulness of DL in positron emission tomography (PET)/computed tomography (CT) for breast cancer (BC) has been insufficiently studied. This study in...

Deep learning-based pancreas volume assessment in individuals with type 1 diabetes.

BMC medical imaging
Pancreas volume is reduced in individuals with diabetes and in autoantibody positive individuals at high risk for developing type 1 diabetes (T1D). Studies investigating pancreas volume are underway to assess pancreas volume in large clinical databas...

Robotic transabdominal retromuscular rectus diastasis (r-TARRD) repair: a new approach.

Hernia : the journal of hernias and abdominal wall surgery
PURPOSE: The aim of this study is to present our innovative robotic approach for the treatment of rectus diastasis with concurrent primary or incisional ventral hernias.

The learning curve to attain surgical competency in robotic colorectal surgery.

ANZ journal of surgery
INTRODUCTION: The aim of the study was to assess the robotic colorectal surgery (RCS) learning curve of an experienced surgeon.

Deep Learning-Assisted Diagnosis of Pediatric Skull Fractures on Plain Radiographs.

Korean journal of radiology
OBJECTIVE: To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children.

Derivation of a natural language processing algorithm to identify febrile infants.

Journal of hospital medicine
BACKGROUND: Diagnostic codes can retrospectively identify samples of febrile infants, but sensitivity is low, resulting in many febrile infants eluding detection. To ensure study samples are representative, an improved approach is needed.

Minimally invasive hysterectomy for benign indications-surgical volume matters: a retrospective cohort study comparing complications of robotic-assisted and conventional laparoscopic hysterectomies.

Journal of robotic surgery
The objective of this study was to evaluate the incidence of perioperative complications in robotic-assisted hysterectomies performed by high-volume robotic surgeons compared to conventional laparoscopic hysterectomies performed by all gynecologic su...

Evaluation of the learning curve for robot-assisted rectal surgery using the cumulative sum method.

Surgical endoscopy
BACKGROUND: There is no clear evidence on the number of cases required to master the techniques required in robot-assisted surgery for different surgical fields and techniques. The purpose of this study was to clarify the learning curve of robot-assi...

Postoperative delirium prediction using machine learning models and preoperative electronic health record data.

BMC anesthesiology
BACKGROUND: Accurate, pragmatic risk stratification for postoperative delirium (POD) is necessary to target preventative resources toward high-risk patients. Machine learning (ML) offers a novel approach to leveraging electronic health record (EHR) d...