AIMC Topic: Retrospective Studies

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Deep Generative Medical Image Harmonization for Improving Cross-Site Generalization in Deep Learning Predictors.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: In the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between e...

Extended robot-assisted laparoscopic prostatectomy and extended pelvic lymph node dissection as a monotherapy in patients with very high-risk prostate cancer Patients.

Cancer medicine
BACKGROUND: Patients with very-high-risk prostate cancer (VHRPCa) have earlier biochemical recurrences (BCRs) and higher mortality rates. It remains unknown whether extended robot-assisted laparoscopic prostatectomy (eRALP) without neoadjuvant or adj...

Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams.

Nature communications
Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates. In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ul...

CheXED: Comparison of a Deep Learning Model to a Clinical Decision Support System for Pneumonia in the Emergency Department.

Journal of thoracic imaging
PURPOSE: Patients with pneumonia often present to the emergency department (ED) and require prompt diagnosis and treatment. Clinical decision support systems for the diagnosis and management of pneumonia are commonly utilized in EDs to improve patien...

Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers.

Scientific reports
This study attempts to explore the radiomics-based features of multi-parametric magnetic resonance imaging (MRI) and construct a machine-learning model to predict the blood supply in vestibular schwannoma preoperatively. By retrospectively collecting...

Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation.

Scientific reports
Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper we aimed t...

A deep-learning model for identifying fresh vertebral compression fractures on digital radiography.

European radiology
OBJECTIVES: To develop a deep-learning (DL) model for identifying fresh VCFs from digital radiography (DR), with magnetic resonance imaging (MRI) as the reference standard.

Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.

Scientific reports
The achievement of the pathologic complete response (pCR) has been considered a metric for the success of neoadjuvant chemotherapy (NAC) and a powerful surrogate indicator of the risk of recurrence and long-term survival. This study aimed to develop ...

Short-term and pathologic outcomes of robotic versus open pancreatoduodenectomy for periampullary and pancreatic head malignancy: an early experience.

Journal of robotic surgery
Open pancreatoduodenectomy (OPD) is associated with high perioperative morbidity. Adoption of robot-assisted pancreatoduodenectomy (RAPD) has been slow despite ergonomic advantages, improved visualization and dexterity. We aim to report our experienc...

Deep Learning for Adjacent Segment Disease at Preoperative MRI for Cervical Radiculopathy.

Radiology
Background Patients who undergo surgery for cervical radiculopathy are at risk for developing adjacent segment disease (ASD). Identifying patients who will develop ASD remains challenging for clinicians. Purpose To develop and validate a deep learnin...