AIMC Topic: Humans

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Combined Deep Learning-based Super-Resolution and Partial Fourier Reconstruction for Gradient Echo Sequences in Abdominal MRI at 3 Tesla: Shortening Breath-Hold Time and Improving Image Sharpness and Lesion Conspicuity.

Academic radiology
RATIONALE AND OBJECTIVES: To investigate the impact of a prototypical deep learning-based super-resolution reconstruction algorithm tailored to partial Fourier acquisitions on acquisition time and image quality for abdominal T1-weighted volume-interp...

Robot-assisted laparoscopy repair of uterine isthmocele: A two-center observational study.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
OBJECTIVE: To analyze outcomes and postoperative complications in patients undergoing robot-assisted isthmocele repair.

Artificial Intelligence System for Automatic Quantitative Analysis and Radiology Reporting of Leg Length Radiographs.

Journal of digital imaging
Leg length discrepancies are common orthopedic problems with the potential for poor functional outcomes. These are frequently assessed using bilateral leg length radiographs. The objective was to determine whether an artificial intelligence (AI)-base...

Artificial intelligence for the automated single-shot assessment of psoriasis severity.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: PASI score is globally used to assess disease activity of psoriasis. However, it is relatively complicated and time-consuming, and the score will vary due to the inconsistent subjectivity between dermatologists. Therefore, an AI system ca...

Predicting the outcome of radiotherapy in brain metastasis by integrating the clinical and MRI-based deep learning features.

Medical physics
BACKGROUND: A considerable proportion of metastatic brain tumors progress locally despite stereotactic radiation treatment, and it can take months before such local progression is evident on follow-up imaging. Prediction of radiotherapy outcome in te...

eICAB: A novel deep learning pipeline for Circle of Willis multiclass segmentation and analysis.

NeuroImage
BACKGROUND: The accurate segmentation, labeling and quantification of cerebral blood vessels on MR imaging is important for basic and clinical research, yet results are not generalizable, and often require user intervention. New methods are needed to...