AIMC Topic: Deep Learning

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[Deep Learning-Based Identification of Common Complication Features of Surgical Incisions].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
OBJECTIVE: In recent years, due to the development of accelerated recovery after surgery and day surgery in the field of surgery, the average length-of-stay of patients has been shortened and patients stay at home for post-surgical recovery and heali...

[Application of Deep Learning Algorithm in the Grading Assessment of Corneal Fluorescein Staining].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
OBJECTIVE: To explore the application value of applying deep learning (DL) algorithm in the grading assessment of corneal fluorescein staining.

[Analysis of the Tumor Immune Microenvironment of Colorectal Cancer by Deep Learning-Based Imaging Cytometry].

Gan to kagaku ryoho. Cancer & chemotherapy
The tumor immune microenvironment(TIME)of colorectal cancer contains indicators of unique therapeutic outcomes for each cancer patient. Deep learning-based imaging cytometry(DL-IC), which can obtain objective and reproducible cell- related informatio...

Deep Learning Super-Resolution Reconstruction for Fast and Motion-Robust T2-weighted Prostate MRI.

Radiology
Background Deep learning (DL) reconstructions can enhance image quality while decreasing MRI acquisition time. However, DL reconstruction methods combined with compressed sensing for prostate MRI have not been well studied. Purpose To use an industry...

Commercially Available Chest Radiograph AI Tools for Detecting Airspace Disease, Pneumothorax, and Pleural Effusion.

Radiology
Background Commercially available artificial intelligence (AI) tools can assist radiologists in interpreting chest radiographs, but their real-life diagnostic accuracy remains unclear. Purpose To evaluate the diagnostic accuracy of four commercially ...

Comparison of the Diagnostic Accuracy of Mammogram-based Deep Learning and Traditional Breast Cancer Risk Models in Patients Who Underwent Supplemental Screening with MRI.

Radiology
Background Access to supplemental screening breast MRI is determined using traditional risk models, which are limited by modest predictive accuracy. Purpose To compare the diagnostic accuracy of a mammogram-based deep learning (DL) risk assessment mo...

Impact of a reduced iodine load with deep learning reconstruction on abdominal MDCT.

Medicine
To evaluate the impact of a reduced iodine load using deep learning reconstruction (DLR) on the hepatic parenchyma compared to conventional iterative reconstruction (hybrid IR) and its consequence on the radiation dose and image quality. This retrosp...