A Deep Learning-Based Segmentation System for Rapid Onsite Cytologic Pathology Evaluation of Pancreatic Masses: A Retrospective, Multicenter, Diagnostic Study

2022 ◽  
Author(s):  
Song Zhang ◽  
Yangfan Zhou ◽  
Dehua Tang ◽  
Muhan Ni ◽  
Jinyu Zheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Study ◽  
Pancreatic Masses

Artificial intelligence based on deep learning for differential diagnosis between benign and malignant pulmonary nodules: A real-world, multicenter, diagnostic study.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 9037-9037
Author(s):  
Tao Xu ◽  
Chuoji Huang ◽  
Yaoqi Liu ◽  
Jing Gao ◽  
Huan Chang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Real World ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Test Method ◽  
Histopathological Diagnosis ◽  
Diagnostic Study

9037 Background: Lung cancer is the most common cancer worldwide. Artificial intelligence (AI) platform using deep learning algorithms have made a remarkable progress in improving diagnostic accuracy of lung cancer. But AI diagnostic performance in identifying benign and malignant pulmonary nodules still needs improvement. We aimed to validate a Pulmonary Nodules Artificial Intelligence Diagnostic System (PNAIDS) by analyzing computed tomography (CT) imaging data. Methods: This real-world, multicentre, diagnostic study was done in five different tier hospitals in China. The CT images of patients, who were aged over 18 years and never had previous anti-cancer treatments, were retrieved from participating hospitals. 534 eligible patients with 5-30mm diameter pulmonary nodules identified by CT were planning to confirm with histopathological diagnosis. The performance of PNAIDS was also compared with respiratory specialists and radiologists with expert or competent degrees of expertise as well as Mayo Clinic’s model by area under the curve (AUC) and evaluated differences by calculating the 95% CIs using the Z-test method. 11 selected participants were tested circulating genetically abnormal cells (CACs) before surgery with doctors suggested. Results: 611 lung CT images from 534 individuals were used to test PNAIDS. The diagnostic accuracy, valued by AUC, in identifying benign and malignant pulmonary nodules was 0.765 (95%CI [0.729 - 0.798]). The diagnostic sensitivity of PNAIDS is 0.630(0.579 – 0.679), specificity is 0.753 (0.693 – 0.807). PNAIDS achieved diagnostic accuracy similar to that of the expert respiratory specialists (AUC difference: 0.0036 [-0.0426 - 0.0497]; p = 0.8801) and superior when compared with Mayo Clinic’s model (0.120 [0.0649 - 0.176], p < 0·0001), expert radiologists (0.0620 [0.0124 - 0.112], p = 0.0142) and competent radiologists (0.0751 [0.0248 - 0.125], p = 0.0034). 11 selected participants were suggested negative in AI results but positive in respiratory specialists’ result. 8 of them were malignant in histopathological diagnosis with tested more than 3 CACs in their blood. Conclusions: PNAIDS achieved high diagnostic accuracy in differential diagnoses between benign and malignant pulmonary nodules, with diagnostic accuracy similar to that of expert respiratory specialists and was superior to that of Mayo Clinic’s model and radiologists. CACs may be able to assist CT-based AI in improving their effectiveness but it still need more data to be proved. Clinical trial information: ChiCTR1900026233.

Fully automated deep learning for knee alignment assessment in lower extremity radiographs: a cross-sectional diagnostic study

2021 ◽  
Author(s):  
Sebastian Simon ◽  
Gilbert M. Schwarz ◽  
Alexander Aichmair ◽  
Bernhard J. H. Frank ◽  
Allan Hummer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lower Extremity ◽  
Diagnostic Study ◽  
Knee Alignment ◽  
Cross Sectional

Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study

2021 ◽  
Vol 22 (1) ◽  
pp. 132-141
Author(s):  
Rikiya Yamashita ◽  
Jin Long ◽  
Teri Longacre ◽  
Lan Peng ◽  
Gerald Berry ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Microsatellite Instability ◽  
Learning Model ◽  
Diagnostic Study ◽  
Deep Learning Model

scholarly journals Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study

2020 ◽  
Vol 21 (2) ◽  
pp. 233-241 ◽  
Author(s):  
Wouter Bulten ◽  
Hans Pinckaers ◽  
Hester van Boven ◽  
Robert Vink ◽  
Thomas de Bel ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Learning System ◽  
Diagnostic Study ◽  
Gleason Grading

scholarly journals Deep learning-based carotid plaque vulnerability classification with multicentre contrast-enhanced ultrasound video: a comparative diagnostic study

BMJ Open ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. e047528
Author(s):  
Yang Guang ◽  
Wen He ◽  
Bin Ning ◽  
Hongxia Zhang ◽  
Chen Yin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Carotid Plaque ◽  
State Of The Art ◽  
Characteristic Curve ◽  
Diagnostic Study ◽  
Contrast Enhanced Ultrasound ◽  
Plaque Vulnerability ◽  
Contrast Enhanced ◽  
Holdout Validation ◽  
Better Than

ObjectivesThe aim of this study was to evaluate the performance of deep learning-based detection and classification of carotid plaque (DL-DCCP) in carotid plaque contrast-enhanced ultrasound (CEUS).Methods and analysisA prospective multicentre study was conducted to assess vulnerability in patients with carotid plaque. Data from 547 potentially eligible patients were prospectively enrolled from 10 hospitals, and 205 patients with CEUS video were finally enrolled for analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the effectiveness of DL-DCCP and two experienced radiologists who manually examined the CEUS video (RA-CEUS) in diagnosing and classifying carotid plaque vulnerability. To evaluate the influence of dynamic video input on the performance of the algorithm, a state-of-the-art deep convolutional neural network (CNN) model for static images (Xception) was compared with DL-DCCP for both training and holdout validation cohorts.ResultsThe AUCs of DL-DCCP were significantly better than those of the experienced radiologists for both the training and holdout validation cohorts (training, DL-DCCP vs RA-CEUS, AUC: 0.85 vs 0.69, p<0.01; holdout validation, DL-DCCP vs RA-CEUS, AUC: 0.87 vs 0.66, p<0.01), that is, also better than the best deep CNN model Xception we had performed, for both the training and holdout validation cohorts (training, DL-DCCP vs Xception, AUC:0.85 vs 0.82, p<0.01; holdout validation, DL-DCCP vs Xception, AUC: 0.87 vs 0.77, p<0.01).ConclusionDL-DCCP shows better overall performance in assessing the vulnerability of carotid atherosclerotic plaques than RA-CEUS. Moreover, with a more powerful network structure and better utilisation of video information, DL-DCCP provided greater diagnostic accuracy than a state-of-the-art static CNN model.Trial registration numberChiCTR1900021846,

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning
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