AIMC Topic: Retrospective Studies

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Application of Deep Learning Techniques for Automated Diagnosis of Non-Syndromic Craniosynostosis Using Skull.

The Journal of craniofacial surgery
Non-syndromic craniosynostosis (NSCS) is a disease, in which a single cranial bone suture is prematurely fused. The early intervention of the disease is associated with a favorable outcome at a later age, so appropriate screening of NSCS is essential...

Workflow Integration of Research AI Tools into a Hospital Radiology Rapid Prototyping Environment.

Journal of digital imaging
The field of artificial intelligence (AI) in medical imaging is undergoing explosive growth, and Radiology is a prime target for innovation. The American College of Radiology Data Science Institute has identified more than 240 specific use cases wher...

Deep Learning for Predictive Analysis of Pediatric Otolaryngology Personal Statements: A Pilot Study.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: The personal statement is often an underutilized aspect of pediatric otolaryngology fellowship applications. In this pilot study, we use deep learning language models to cluster personal statements and elucidate their relationship to appli...

An artificial intelligence system using maximum intensity projection MR images facilitates classification of non-mass enhancement breast lesions.

European radiology
OBJECTIVES: To build an artificial intelligence (AI) system to classify benign and malignant non-mass enhancement (NME) lesions using maximum intensity projection (MIP) of early post-contrast subtracted breast MR images.

Differentiation between subchondral insufficiency fractures and advanced osteoarthritis of the knee using transfer learning and an ensemble of convolutional neural networks.

Injury
PURPOSE: Subchondral insufficiency fractures (SIF) and advanced osteoarthritis (OA) of the knee are usually seen in conjunction with bone marrow lesions (BMLs) and their differentiation may pose a significant diagnostic challenge. We aimed to develop...

Artificial Intelligence for Automated Implant Identification in Total Hip Arthroplasty: A Multicenter External Validation Study Exceeding Two Million Plain Radiographs.

The Journal of arthroplasty
BACKGROUND: The surgical management of complications after total hip arthroplasty (THA) necessitates accurate identification of the femoral implant manufacturer and model. Automated image processing using deep learning has been previously developed a...

Robotic versus hand-assisted laparoscopic living donor nephrectomy: comparison of two minimally invasive techniques in kidney transplantation.

Journal of robotic surgery
Robot-assisted donor nephrectomy (RDN) is increasingly used due to its advantages such as its precision and reduced learning curve when compared to laparoscopic techniques. Concerns remain among surgeons regarding possible longer warm ischemia time. ...

Implementing the Robotic deep inferior epigastric perforator Flap in daily practice: A series of 10 cases.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS
The deep inferior epigastric perforator (DIEP) flap is the workhorse in microvascular breast reconstruction. Rectus muscle sacrifice or denervation and inappropriate rectus sheath closure are the main causes of abdominal wall morbidity. Robotic vesse...

Determining the anatomical site in knee radiographs using deep learning.

Scientific reports
An important quality criterion for radiographs is the correct anatomical side marking. A deep neural network is evaluated to predict the correct anatomical side in radiographs of the knee acquired in anterior-posterior direction. In this retrospectiv...

A deep learning approach for detection of shallow anterior chamber depth based on the hidden features of fundus photographs.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Patients with angle-closure glaucoma (ACG) are asymptomatic until they experience a painful attack. Shallow anterior chamber depth (ACD) is considered a significant risk factor for ACG. We propose a deep learning approach t...