Development and Validation of a Deep Learning-Based Survival Prediction Algorithm in Patients with COPD Utilizing Chest Radiographs

2021 ◽  
Author(s):  
Ju Gang Nam ◽  
Hye-Rin Kang ◽  
Sang Min Lee ◽  
Hyungjin Kim ◽  
Chanyoung Rhee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chest Radiographs ◽  
Prediction Algorithm ◽  
Survival Prediction ◽  
Development And Validation

scholarly journals Development and Validation of a Deep Learning–Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs

2019 ◽  
Vol 2 (3) ◽  
pp. e191095 ◽  
Author(s):  
Eui Jin Hwang ◽  
Sunggyun Park ◽  
Kwang-Nam Jin ◽  
Jung Im Kim ◽  
So Young Choi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection Algorithm ◽  
Automated Detection ◽  
Chest Radiographs ◽  
Thoracic Diseases ◽  
Development And Validation

scholarly journals Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs

2020 ◽  
pp. 2003061
Author(s):  
Ju Gang Nam ◽  
Minchul Kim ◽  
Jongchan Park ◽  
Eui Jin Hwang ◽  
Jong Hyuk Lee ◽  
...  
Keyword(s):  
Emergency Department ◽  
Deep Learning ◽  
Learning Algorithm ◽  
External Validation ◽  
Chest Radiographs ◽  
Reading Tests ◽  
Deep Learning Algorithm ◽  
The Mean ◽  
Emergency Department Patients ◽  
Development And Validation

We aimed to develop a deep-learning algorithm detecting 10 common abnormalities (DLAD-10) on chest radiographs and to evaluate its impact in diagnostic accuracy, timeliness of reporting, and workflow efficacy.DLAD-10 was trained with 146 717 radiographs from 108 053 patients using a ResNet34-based neural network with lesion-specific channels for 10 common radiologic abnormalities (pneumothorax, mediastinal widening, pneumoperitoneum, nodule/mass, consolidation, pleural effusion, linear atelectasis, fibrosis, calcification, and cardiomegaly). For external validation, the performance of DLAD-10 on a same-day CT-confirmed dataset (normal:abnormal, 53:147) and an open-source dataset (PadChest; normal:abnormal, 339:334) was compared to that of three radiologists. Separate simulated reading tests were conducted on another dataset adjusted to real-world disease prevalence in the emergency department, consisting of four critical, 52 urgent, and 146 non-urgent cases. Six radiologists participated in the simulated reading sessions with and without DLAD-10.DLAD-10 exhibited areas under the receiver-operating characteristic curves (AUROCs) of 0.895–1.00 in the CT-confirmed dataset and 0.913–0.997 in the PadChest dataset. DLAD-10 correctly classified significantly more critical abnormalities (95.0% [57/60]) than pooled radiologists (84.4% [152/180]; p=0.01). In simulated reading tests for emergency department patients, pooled readers detected significantly more critical (70.8% [17/24] versus 29.2% [7/24]; p=0.006) and urgent (82.7% [258/312] versus 78.2% [244/312]; p=0.04) abnormalities when aided by DLAD-10. DLAD-10 assistance shortened the mean time-to-report critical and urgent radiographs (640.5±466.3 versus 3371.0±1352.5 s and 1840.3±1141.1 versus 2127.1±1468.2, respectively; p-values<0.01) and reduced the mean interpretation time (20.5±22.8 versus 23.5±23.7 s; p<0.001).DLAD-10 showed excellent performance, improving radiologists' performance and shortening the reporting time for critical and urgent cases.

scholarly journals A deep-learning-based prediction of refractive error using photorefraction images captured by smartphone: Model Development and Validation Study. (Preprint)

2019 ◽  
Author(s):  
Jaehyeong Chun ◽  
Youngjun Kim ◽  
Kyungyoon Shin ◽  
Sun Hyup Han ◽  
Sei Yeul Oh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Refractive Error ◽  
Model Development ◽  
Screening Tools ◽  
Prediction Algorithm ◽  
Easy Access ◽  
Number Of Patients ◽  
Prediction Systems ◽  
Development And Validation ◽  
Efficient Screening

BACKGROUND Accurately predicting refractive error in children is crucial for detecting amblyopia, which can lead to permanent visual impairment, but is potentially curable if detected early. Various tools have been adopted to more easily screen a larger number of patients for amblyopia risk. OBJECTIVE For efficient screening, easy access to screening tools and an accurate prediction algorithm are the most important factors. In this study, we developed an automated deep-learning-based system to predict the range of refractive error in children (mean age: 4.32±1.87 years) using 305 eccentric photorefraction images captured with a smartphone. METHODS Photorefraction images were divided into seven classes according to their spherical values as measured by cycloplegic refraction. RESULTS The trained deep-learning models resulted in an overall accuracy of 81.6%, with the following accuracy for each refractive error class: 80.0% in ≤ -5.0 diopters (D), 77.8% in > -5.0 D and ≤ -3.0 D, 82.0% in > -3.0 D and ≤ -0.5 D, 83.3% in > -0.5 D and < +0.5 D, 82.8% in ≥ +0.5 D and < +3.0 D, 79.3% in ≥ +3.0 D and < +5.0 D, and 75.0% in ≥ +5.0 D. These results indicate that our deep-learning-based system performed sufficiently accurately. CONCLUSIONS This study demonstrated the potential for precise smartphone-based prediction systems for refractive error using deep learning and, further, yielded a robust collection of pediatric photorefraction images. CLINICALTRIAL

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