Clinical impact of a deep learning system for automated detection of missed pulmonary nodules on routine body computed tomography including the chest region

Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from seven cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the deep learning system, making it learn lung features. Following this, 1266 patients (924 with COVID-19 (471 had follow-up for >5 days) and 342 with other pneumonia) from six cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the four external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC 0.87 and 0.88, respectively) and viral pneumonia (AUC 0.86). Moreover, the deep learning system succeeded to stratify patients into high- and low-risk groups whose hospital-stay time had significant difference (p=0.013 and p=0.014, respectively). Without human assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening of COVID-19 and identifying potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.

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Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age‐related macular degeneration

Acta Ophthalmologica ◽

10.1111/aos.14306 ◽

2019 ◽

Vol 98 (4) ◽

pp. 368-377 ◽

Cited By ~ 4

Author(s):

Cristina González‐Gonzalo ◽

Verónica Sánchez‐Gutiérrez ◽

Paula Hernández‐Martínez ◽

Inés Contreras ◽

Yara T. Lechanteur ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Deep Learning ◽

Macular Degeneration ◽

Automated Detection ◽

Learning System ◽

Age Related Macular Degeneration ◽

Age Related

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Feasibility of Three-Dimensional Deep Learning for Disease Differentiation of Solitary Pulmonary Nodules on Computed Tomography Scans

10.1164/ajrccm-conference.2020.201.1_meetingabstracts.a7689 ◽

2020 ◽

Author(s):

J. Deng ◽

Y. She ◽

W. Zhou ◽

Q. Lin ◽

C. Chen

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Three Dimensional ◽

Pulmonary Nodules ◽

Solitary Pulmonary Nodules ◽

Computed Tomography Scans

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A Simple Method to Train the AI Diagnosis Model of Pulmonary Nodules

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/2812874 ◽

2020 ◽

Vol 2020 ◽

pp. 1-6

Author(s):

Zhehao He ◽

Wang Lv ◽

Jian Hu

Keyword(s):

Deep Learning ◽

Data Augmentation ◽

Pulmonary Nodules ◽

Learning System ◽

Pathological Diagnosis ◽

Classification Model ◽

Lung Nodules ◽

Training Set ◽

Simple Method ◽

Test Set

Background. The differential diagnosis of subcentimetre lung nodules with a diameter of less than 1 cm has always been one of the problems of imaging doctors and thoracic surgeons. We plan to create a deep learning model for the diagnosis of pulmonary nodules in a simple method. Methods. Image data and pathological diagnosis of patients come from the First Affiliated Hospital of Zhejiang University School of Medicine from October 1, 2016, to October 1, 2019. After data preprocessing and data augmentation, the training set is used to train the model. The test set is used to evaluate the trained model. At the same time, the clinician will also diagnose the test set. Results. A total of 2,295 images of 496 lung nodules and their corresponding pathological diagnosis were selected as a training set and test set. After data augmentation, the number of training set images reached 12,510 images, including 6,648 malignant nodular images and 5,862 benign nodular images. The area under the P-R curve of the trained model is 0.836 in the classification of malignant and benign nodules. The area under the ROC curve of the trained model is 0.896 (95% CI: 78.96%~100.18%), which is higher than that of three doctors. However, the P value is not less than 0.05. Conclusion. With the help of an automatic machine learning system, clinicians can create a deep learning pulmonary nodule pathology classification model without the help of deep learning experts. The diagnostic efficiency of this model is not inferior to that of the clinician.

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A Fully Automatic Deep Learning System for COVID-19 Diagnostic and Prognostic Analysis

10.1101/2020.03.24.20042317 ◽

2020 ◽

Cited By ~ 10

Author(s):

Shuo Wang ◽

Yunfei Zha ◽

Weimin Li ◽

Qingxia Wu ◽

Xiaohu Li ◽

...

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

High Risk ◽

Learning System ◽

High Risk Patients ◽

Prognostic Analysis ◽

Computed Tomography Images ◽

Risk Patients ◽

Fully Automatic ◽

Worse Prognosis

AbstractCoronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19, and finding high-risk patients with worse prognosis for early prevention and medical resources optimization is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from 7 cities or provinces. Firstly, 4106 patients with computed tomography images and gene information were used to pre-train the DL system, making it learn lung features. Afterwards, 1266 patients (924 with COVID-19, and 471 had follow-up for 5+ days; 342 with other pneumonia) from 6 cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the 4 external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC=0.87 and 0.88) and viral pneumonia (AUC=0.86). Moreover, the deep learning system succeeded to stratify patients into high-risk and low-risk groups whose hospital-stay time have significant difference (p=0.013 and 0.014). Without human-assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening COVID-19 and finding potential high-risk patients, which may be helpful for medical resource optimization and early prevention before patients show severe symptoms.Take-home messageFully automatic deep learning system provides a convenient method for COVID-19 diagnostic and prognostic analysis, which can help COVID-19 screening and finding potential high-risk patients with worse prognosis.

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A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study

Journal of Dentistry ◽

10.1016/j.jdent.2021.103865 ◽

2021 ◽

pp. 103865

Author(s):

Eman Shaheen ◽

André Leite ◽

Khalid Ayidh Alqahtani ◽

Andreas Smolders ◽

Adriaan Van Gerven ◽

...

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Cone Beam Computed Tomography ◽

Validation Study ◽

Learning System ◽

Cone Beam ◽

Tooth Segmentation

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Auxiliary diagnosis of pulmonary nodules using liquid biopsy and deep learning/techniques.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e13154 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e13154-e13154

Author(s):

Li Bai ◽

Yanqing Zhou ◽

Yaru Chen ◽

Quanxing Liu ◽

Dong Zhou ◽

...

Keyword(s):

Image Analysis ◽

Deep Learning ◽

Diagnostic Accuracy ◽

Cancer Patients ◽

Liquid Biopsy ◽

Pulmonary Nodules ◽

Ct Images ◽

Learning System ◽

Benign Tumors ◽

Deep Convolutional Neural Networks

e13154 Background: Many people harbor pulmonary nodules. Such nodules can be detected by low-dose computed tomography (LDCT) during regular physical examinations. If a pulmonary nodule is small (i.e. < 10mm), it is very difficult to diagnose whether it is benign or malignant using CT images alone. To address this problem, we developed a method based on liquid biopsy and deep learning to improve diagnostic accuracy of pulmonary nodules. Methods: Thirty-eight patientsharboring one or more small pulmonary nodules were enrolled in this study. Twenty-nine patients were diagnosed as having cancer (stage I = 21, stage II = 1, stage III = 3, stage IV = 4) using tissue biopsy, while the other 9 patients were diagnosed as having benign tumors or lung diseases other than cancer. For each patient, a blood sample was obtained prior to biopsy, and the cell free DNA (cfDNA) was sequenced using a 451-gene panel to a depth of 20,000×. The unique molecular identifiers (UMI) technique was applied to reduce false positives. Seventeen patients also had full-resolution CT images available. A deep learning system primarily based on deep convolutional neural networks (CNN) was used to analyze these CT images. Results: Sequence analysis of blood samples revealed that 75.8% (22/29) of cancer patients had detectable cancer related mutations, and only 1 of 9 (11.1%) non-cancer patient was found to carry a TP53 mutation. The most frequent mutations seen in cancer patients involved genes TP53 (N = 11), EGFR (N = 7), and KRAS (N = 3) with mutant allele fractions varying from 0.08% to 74.77%. Deep learning analysis of the 17 available CT images correctly identified cancers in 88.2% (15/17) of patients. However, by combining the liquid biopsy and image analysis results, all 17 patients were correctly diagnosed. Conclusions: Deep learning-based analysis of CT images can be applied to early diagnosis of lung cancers; but the accuracy of image analysis, when used alone, is only moderate. Diagnostic accuracy can be greatly improved using liquid biopsy as an auxiliary method in patients with pulmonary nodules.

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