AIMC Topic: Retrospective Studies

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Are there disparities in access to robot-assisted laparoscopic surgery among pediatric urology patients? US institutional experience.

International journal of urology : official journal of the Japanese Urological Association
OBJECTIVE: Literature suggests access to robotic surgery varies by race and payer status. We seek to investigate whether disparities exist in robot-assisted laparoscopic surgery among the pediatric urology population at our tertiary academic medical ...

Dose prediction via distance-guided deep learning: Initial development for nasopharyngeal carcinoma radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Geometric information such as distance information is essential for dose calculations in radiotherapy. However, state-of-the-art dose prediction methods use only binary masks without distance information. This study aims to de...

Automatic Cardiac Structure Contouring for Small Datasets with Cascaded Deep Learning Models.

Journal of medical systems
Cardiac structure contouring is a time consuming and tedious manual activity used for radiotherapeutic dose toxicity planning. We developed an automatic cardiac structure segmentation pipeline for use in low-dose non-contrast planning CT based on dee...

Artificial intelligence - can it be used to outsmart oral cancer?

Evidence-based dentistry
Data Sources Electronic search on PubMed, Cochrane, Scopus, Embase, Google Scholar, Saudi Digital Library and Web of Science, and hand searching carried out for studies published January 2000-March 2021. Language was restricted to English.Study selec...

The robotic learning curve for a newly appointed colorectal surgeon.

Journal of robotic surgery
Robotic colorectal surgery allows for better ergonomics, superior retraction, and fine movements in the narrow anatomy of the pelvis. Recent years have seen the uptake of robotic surgery in all pelvic surgeries specifically in low rectal malignancies...

Evaluation of deep learning reconstructed high-resolution 3D lumbar spine MRI.

European radiology
OBJECTIVES: To compare interobserver agreement and image quality of 3D T2-weighted fast spin echo (T2w-FSE) L-spine MRI images processed with a deep learning reconstruction (DLRecon) against standard-of-care (SOC) reconstruction, as well as against 2...

Single-position robot-assisted versus laparoscopic antegrade bilateral inguinal lymphadenectomy for penile cancer: A retrospective controlled study.

Asian journal of surgery
OBJECTIVES: The main purpose of this study was to compare the surgical strategy and clinical outcomes of single-position robotic assisted laparoscopic anterograde bilateral inguinal lymphadenectomy for penile cancer.

A retrospective analysis using deep-learning models for prediction of survival outcome and benefit of adjuvant chemotherapy in stage II/III colorectal cancer.

Journal of cancer research and clinical oncology
PURPOSE: Most of Stage II/III colorectal cancer (CRC) patients can be cured by surgery alone, and only certain CRC patients benefit from adjuvant chemotherapy. Risk stratification based on deep-learning from haematoxylin and eosin (H&E) images has be...

Diagnostic accuracy of a commercially available deep-learning algorithm in supine chest radiographs following trauma.

The British journal of radiology
OBJECTIVES: Trauma chest radiographs may contain subtle and time-critical pathology. Artificial intelligence (AI) may aid in accurate reporting, timely identification and worklist prioritisation. However, few AI programs have been externally validate...

Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Journal of the American Heart Association
Background Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction ...