Development of a deep learning‐based patient‐specific target contour prediction model for markerless tumor positioning

2022 ◽  
Author(s):  
Dejun Zhou ◽  
Mitsuhiro Nakamura ◽  
Nobutaka Mukumoto ◽  
Michio Yoshimura ◽  
Takashi Mizowaki
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Patient Specific ◽  
Specific Target

A deep learning-based prediction model for gamma evaluation in patient-specific quality assurance

2018 ◽  
Vol 45 (9) ◽  
pp. 4055-4065 ◽  
Author(s):  
Seiji Tomori ◽  
Noriyuki Kadoya ◽  
Yoshiki Takayama ◽  
Tomohiro Kajikawa ◽  
Katsumi Shima ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Deep Learning ◽  
Prediction Model ◽  
Patient Specific ◽  
Gamma Evaluation

A Deep Learning Risk Prediction Model for Overall Survival in Patients with Gastric Cancer: A Multicenter Study

2020 ◽  
Author(s):  
Liwen Zhang ◽  
Di Dong ◽  
Wenjuan Zhang ◽  
Xiaohan Hao ◽  
Mengjie Fang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Deep Learning ◽  
Prediction Model ◽  
Risk Prediction ◽  
Multicenter Study ◽  
Risk Prediction Model

scholarly journals Assessment of patient specific information in the wild on fundus photography and optical coherence tomography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marion R. Munk ◽  
Thomas Kurmann ◽  
Pablo Márquez-Neila ◽  
Martin S. Zinkernagel ◽  
Sebastian Wolf ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cross Sections ◽  
Underlying Disease ◽  
Geographic Atrophy ◽  
Clinical Population ◽  
Fundus Photography ◽  
Peak Performance ◽  
Patient Specific ◽  
Specific Information ◽  
Fundus Images

AbstractIn this paper we analyse the performance of machine learning methods in predicting patient information such as age or sex solely from retinal imaging modalities in a heterogeneous clinical population. Our dataset consists of N = 135,667 fundus images and N = 85,536 volumetric OCT scans. Deep learning models were trained to predict the patient’s age and sex from fundus images, OCT cross sections and OCT volumes. For sex prediction, a ROC AUC of 0.80 was achieved for fundus images, 0.84 for OCT cross sections and 0.90 for OCT volumes. Age prediction mean absolute errors of 6.328 years for fundus, 5.625 years for OCT cross sections and 4.541 for OCT volumes were observed. We assess the performance of OCT scans containing different biomarkers and note a peak performance of AUC = 0.88 for OCT cross sections and 0.95 for volumes when there is no pathology on scans. Performance drops in case of drusen, fibrovascular pigment epitheliuum detachment and geographic atrophy present. We conclude that deep learning based methods are capable of classifying the patient’s sex and age from color fundus photography and OCT for a broad spectrum of patients irrespective of underlying disease or image quality. Non-random sex prediction using fundus images seems only possible if the eye fovea and optic disc are visible.

scholarly journals Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Gaoyang Li ◽  
Haoran Wang ◽  
Mingzi Zhang ◽  
Simon Tupin ◽  
Aike Qiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Artery Bypass ◽  
Computational Cost ◽  
Specific Model ◽  
Arterial System ◽  
Patient Specific ◽  
Cardiovascular Hemodynamics ◽  
Before And After ◽  
Deep Learning Network ◽  
Cfd Method

AbstractThe clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.

EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning

Energy ◽  
2020 ◽  
pp. 119692
Author(s):  
Xiaosheng Peng ◽  
Hongyu Wang ◽  
Jianxun Lang ◽  
Wenze Li ◽  
Qiyou Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Wind Power ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Power Prediction ◽  
Numerical Weather ◽  
Model Based ◽  
Wind Power Prediction
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