AIMC Topic: Retrospective Studies

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Explainable deep learning model to predict invasive bacterial infection in febrile young infants: A retrospective study.

International journal of medical informatics
BACKGROUND: Machine learning models have demonstrated superior performance in predicting invasive bacterial infection (IBI) in febrile infants compared to commonly used risk stratification criteria in recent studies. However, the black-box nature of ...

"Human vs Machine" Validation of a Deep Learning Algorithm for Pediatric Middle Ear Infection Diagnosis.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: We compared the diagnostic performance of human clinicians with that of a neural network algorithm developed using a library of tympanic membrane images derived from children taken to the operating room with the intent of performing myring...

Robot-assisted TAMIS: a systematic review of feasibility and outcomes.

Surgical endoscopy
BACKGROUND: In the advancement of transanal local excision, robot-assisted transanal minimal invasive surgery is the newest development. In the confined area of the rectum, robot-assisted surgery should, theoretically, be superior due to articulated ...

Detecting dry eye from ocular surface videos based on deep learning.

The ocular surface
OBJECTIVE: To assess the performance of convolutional neural networks (CNNs) for automated diagnosis of dry eye (DE) in patients undergoing video keratoscopy based on single ocular surface video frames.

Robot-assisted versus navigated transpedicular spine fusion: A comparative study.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: The aim of this study was to compare the intraoperative and postoperative outcomes between a robot-assisted versus a navigated transpedicular fusion technique.

Effect of Contrast Level and Image Format on a Deep Learning Algorithm for the Detection of Pneumothorax with Chest Radiography.

Journal of digital imaging
Under the black-box nature in the deep learning model, it is uncertain how the change in contrast level and format affects the performance. We aimed to investigate the effect of contrast level and image format on the effectiveness of deep learning fo...

MRI-based two-stage deep learning model for automatic detection and segmentation of brain metastases.

European radiology
OBJECTIVES: To develop and validate a two-stage deep learning model for automatic detection and segmentation of brain metastases (BMs) in MRI images.

Robot-assisted laparoscopic cystectomy with non-continent urinary diversion for neurogenic lower urinary tract dysfunction: Midterm outcomes.

Neurourology and urodynamics
OBJECTIVES: The aim of this study was to assess midterm functional outcomes and complications of robot-assisted laparoscopic cystectomy with non-continent urinary diversion in patients with neurogenic lower urinary tract dysfunction.

Detection of the separated root canal instrument on panoramic radiograph: a comparison of LSTM and CNN deep learning methods.

Dento maxillo facial radiology
OBJECTIVES: A separated endodontic instrument is one of the challenging complications of root canal treatment. The purpose of this study was to compare two deep learning methods that are convolutional neural network (CNN) and long short-term memory (...

Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Determination of H3 K27M mutation in diffuse midline glioma (DMG) is key for prognostic assessment and stratifying patient subgroups for clinical trials. MRI can noninvasively depict morphological and metabolic characteristics of H3 K27M ...