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

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.

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.

scholarly journals Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review

Data ◽  
2018 ◽  
Vol 3 (3) ◽  
pp. 28 ◽  
Author(s):  
Kasthurirangan Gopalakrishnan
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
Asset Management ◽  
Network Architecture ◽  
Crack Detection ◽  
Future Research ◽  
Deep Convolutional Neural Networks ◽  
Pavement Distress ◽  
Learning Software

Deep learning, more specifically deep convolutional neural networks, is fast becoming a popular choice for computer vision-based automated pavement distress detection. While pavement image analysis has been extensively researched over the past three decades or so, recent ground-breaking achievements of deep learning algorithms in the areas of machine translation, speech recognition, and computer vision has sparked interest in the application of deep learning to automated detection of distresses in pavement images. This paper provides a narrative review of recently published studies in this field, highlighting the current achievements and challenges. A comparison of the deep learning software frameworks, network architecture, hyper-parameters employed by each study, and crack detection performance is provided, which is expected to provide a good foundation for driving further research on this important topic in the context of smart pavement or asset management systems. The review concludes with potential avenues for future research; especially in the application of deep learning to not only detect, but also characterize the type, extent, and severity of distresses from 2D and 3D pavement images.

OCTAI: Smartphone-based Optical Coherence Tomography Image Analysis System

2021 ◽  
Author(s):  
Adrit Rao ◽  
Harvey A. Fishman
Keyword(s):  
Optical Coherence Tomography ◽  
Image Analysis ◽  
Deep Learning ◽  
Early Stage ◽  
Real Life ◽  
Learning System ◽  
Optical Coherence Tomography Image ◽  
Image Analysis System ◽  
Optical Coherence ◽  
Oct Image

Identifying diseases in Optical Coherence Tomography (OCT) images using Deep Learning models and methods is emerging as a powerful technique to enhance clinical diagnosis. Identifying macular diseases in the eye at an early stage and preventing misdiagnosis is crucial. The current methods developed for OCT image analysis have not yet been integrated into an accessible form-factor that can be utilized in a real-life scenario by Ophthalmologists. Additionally, current methods do not employ robust multiple metric feedback. This paper proposes a highly accurate smartphone-based Deep Learning system, OCTAI, that allows a user to take an OCT picture and receive real-time feedback through on-device inference. OCTAI analyzes the input OCT image in three different ways: (1) full image analysis, (2) quadrant based analysis, and (3) disease detection based analysis. With these three analysis methods, along with an Ophthalmologist's interpretation, a robust diagnosis can potentially be made. The ultimate goal of OCTAI is to assist Ophthalmologists in making a diagnosis through a digital second opinion and enabling them to cross-check their diagnosis before making a decision based on purely manual analysis of OCT images. OCTAI has the potential to allow Ophthalmologists to improve their diagnosis and may reduce misdiagnosis rates, leading to faster treatment of diseases.

Medical Sports Rehabilitation Deep Learning System of Sports Injury Based on MRI Image Analysis

2020 ◽  
Vol 10 (5) ◽  
pp. 1091-1097 ◽  
Author(s):  
Hongbing Ba
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Circulatory System ◽  
Sports Injury ◽  
Learning System ◽  
Experimental Result ◽  
Static State ◽  
Prevention Campaign ◽  
The Central Nervous System ◽  
Mri Image

Medical sports rehabilitation deep learning system of sports injury based on MRI image analysis is proposed in this paper. Preparation activities are various body exercises that are purposely performed before physical education, training, and competition. It is a transitional phase from the static state to the moving state of the human body. Preparatory activities can improve the excitability of the central nervous system, improve the ability of the cerebral cortex to analyze and judge movements, and thus make the movement more coordinated and accurate. At the same time prepare activity can also improve the respiratory and circulatory system functions and reduce the muscles, ligaments of the sticky nature and the contraction of muscles for speed and strength, in order to maximize the capacity of the physical movement and injury prevention campaign ready. Therefore, how to use the MRI image to numerically analyze the mentioned task is essential. We integrate the deep learning model to propose the novel image enhancement and recognition model to undertake the task of medical sports rehabilitation system. The experimental result proves the performance is robust.

scholarly journals Detection of Pulmonary Nodules in ct Images Using Deep Learning Technique

2020 ◽  
Vol 16 (4) ◽  
pp. 568-575
Author(s):  
Santhi Balachandran ◽  
Divya ◽  
Nithya Rajendran ◽  
Brindha Giri
Keyword(s):  
Deep Learning ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Learning Technique

scholarly journals Deep learning for early detection of pathological changes in X-ray bone microstructures: case of osteoarthritis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Livija Jakaite ◽  
Vitaly Schetinin ◽  
Jiří Hladůvka ◽  
Sergey Minaev ◽  
Aziz Ambia ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Early Stage ◽  
Medical Image Analysis ◽  
Texture Features ◽  
Group Method ◽  
X Ray ◽  
Number Of Patients ◽  
Image Pixels

AbstractTexture features are designed to quantitatively evaluate patterns of spatial distribution of image pixels for purposes of image analysis and interpretation. Unexplained variations in the texture patterns often lead to misinterpretation and undesirable consequences in medical image analysis. In this paper we explore the ability of machine learning (ML) methods to design a radiology test of Osteoarthritis (OA) at early stage when the number of patients’ cases is small. In our experiments we use high-resolution X-ray images of knees in patients which were identified with Kellgren–Lawrence scores progressing from 1. The existing ML methods have provided a limited diagnostic accuracy, whilst the proposed Group Method of Data Handling strategy of Deep Learning has significantly extended the diagnostic test. The comparative experiments demonstrate that the proposed framework using the Zernike-based texture features has significantly improved the diagnostic accuracy on average by 11%. This allows us to conclude that the designed model for early diagnostic of OA will provide more accurate radiology tests, although new study is required when a large number of patients’ cases will be available.

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

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.

scholarly journals Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study

2020 ◽  
Vol 2020 ◽  
pp. 1-7 ◽  
Author(s):  
Ehsan Vaghefi ◽  
Sophie Hill ◽  
Hannah M. Kersten ◽  
David Squirrell
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Fundus Photography ◽  
Multiple Modalities ◽  
Age Related ◽  
Dry Amd ◽  
Fundus Photographs

Background and Objective. To determine if using a multi-input deep learning approach in the image analysis of optical coherence tomography (OCT), OCT angiography (OCT-A), and colour fundus photographs increases the accuracy of a CNN to diagnose intermediate dry age-related macular degeneration (AMD). Patients and Methods. Seventy-five participants were recruited and divided into three cohorts: young healthy (YH), old healthy (OH), and patients with intermediate dry AMD. Colour fundus photography, OCT, and OCT-A scans were performed. The convolutional neural network (CNN) was trained on multiple image modalities at the same time. Results. The CNN trained using OCT alone showed a diagnostic accuracy of 94%, whilst the OCT-A trained CNN resulted in an accuracy of 91%. When multiple modalities were combined, the CNN accuracy increased to 96% in the AMD cohort. Conclusions. Here we demonstrate that superior diagnostic accuracy can be achieved when deep learning is combined with multimodal image analysis.

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