Development of a deep learning-based algorithm for the automatic detection and quantification of aortic valve calcium

BACKGROUND Detection and quantification of intraabdominal free fluid (i.e., ascites) on computed tomography (CT) are essential processes to find emergent or urgent conditions in patients. In an emergent department, automatic detection and quantification of ascites will be beneficial. OBJECTIVE We aimed to develop an artificial intelligence (AI) algorithm for the automatic detection and quantification of ascites simultaneously using a single deep learning model (DLM). METHODS 2D deep learning models (DLMs) based on a deep residual U-Net, U-Net, bi-directional U-Net, and recurrent residual U-net were developed to segment areas of ascites on an abdominopelvic CT. Based on segmentation results, the DLMs detected ascites by classifying CT images into ascites images and non-ascites images. The AI algorithms were trained using 6,337 CT images from 160 subjects (80 with ascites and 80 without ascites) and tested using 1,635 CT images from 40 subjects (20 with ascites and 20 without ascites). The performance of AI algorithms was evaluated for diagnostic accuracy of ascites detection and for segmentation accuracy of ascites areas. Of these DLMs, we proposed an AI algorithm with the best performance. RESULTS The segmentation accuracy was the highest in the deep residual U-Net with a mean intersection over union (mIoU) value of 0.87, followed by U-Net, bi-directional U-Net, and recurrent residual U-net (mIoU values 0.80, 0.77, and 0.67, respectively). The detection accuracy was the highest in the deep residual U-net (0.96), followed by U-Net, bi-directional U-net, and recurrent residual U-net (0.90, 0.88, and 0.82, respectively). The deep residual U-net also achieved high sensitivity (0.96) and high specificity (0.96). CONCLUSIONS We propose the deep residual U-net-based AI algorithm for automatic detection and quantification of ascites on abdominopelvic CT scans, which provides excellent performance.

Download Full-text

Deep Learning-based Automatic Detection of Radiopaque Markers for Measuring Colon Transit Time

The Journal of Next-generation Convergence Technology Association ◽

10.33097/jncta.2020.04.04.361 ◽

2020 ◽

Vol 4 (4) ◽

pp. 361-366

Author(s):

Chansu Lee ◽

◽

Yehoon Jang ◽

Namgi Kim

Keyword(s):

Deep Learning ◽

Transit Time ◽

Automatic Detection ◽

Colon Transit Time ◽

Radiopaque Markers ◽

Colon Transit

Download Full-text

Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal

Sensors ◽

10.3390/s20061579 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1579

Author(s):

Dongqi Wang ◽

Qinghua Meng ◽

Dongming Chen ◽

Hupo Zhang ◽

Lisheng Xu

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Deep Neural Networks ◽

Channel Model ◽

Expert Knowledge ◽

Automatic Detection ◽

Data Representation ◽

Learning Technology ◽

Arrhythmia Detection ◽

Automatic Feature Extraction

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

Download Full-text

Automatic detection of papilledema through fundus retinal images using deep learning

Microscopy Research and Technique ◽

10.1002/jemt.23865 ◽

2021 ◽

Author(s):

Tanzila Saba ◽

Shahzad Akbar ◽

Hoshang Kolivand ◽

Saeed Ali Bahaj

Keyword(s):

Deep Learning ◽

Automatic Detection ◽

Retinal Images

Download Full-text

Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning

Journal of Digital Imaging ◽

10.1007/s10278-021-00477-8 ◽

2021 ◽

Author(s):

Ranjana Agrawal ◽

Sucheta Kulkarni ◽

Rahee Walambe ◽

Ketan Kotecha

Keyword(s):

Deep Learning ◽

Retinopathy Of Prematurity ◽

Automatic Detection

Download Full-text

Automatic Detection of Thyroid and Adrenal Incidentals Using Radiology Reports and Deep Learning

Journal of Surgical Research ◽

10.1016/j.jss.2021.03.060 ◽

2021 ◽

Vol 266 ◽

pp. 192-200

Author(s):

Stephen P. Canton ◽

Esmaeel Dadashzadeh ◽

Linwah Yip ◽

Raquel Forsythe ◽

Robert Handzel

Keyword(s):

Deep Learning ◽

Automatic Detection ◽

Radiology Reports

Download Full-text

A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone

Drones ◽

10.3390/drones5010006 ◽

2021 ◽

Vol 5 (1) ◽

pp. 6

Author(s):

Apostolos Papakonstantinou ◽

Marios Batsaris ◽

Spyros Spondylidis ◽

Konstantinos Topouzelis

Keyword(s):

Deep Learning ◽

Data Acquisition ◽

Citizen Science ◽

Coastal Zone ◽

Automatic Detection ◽

Monitoring Methods ◽

Monitoring Method ◽

Marine Litter ◽

Density Maps ◽

Learning Techniques

Marine litter (ML) accumulation in the coastal zone has been recognized as a major problem in our time, as it can dramatically affect the environment, marine ecosystems, and coastal communities. Existing monitoring methods fail to respond to the spatiotemporal changes and dynamics of ML concentrations. Recent works showed that unmanned aerial systems (UAS), along with computer vision methods, provide a feasible alternative for ML monitoring. In this context, we proposed a citizen science UAS data acquisition and annotation protocol combined with deep learning techniques for the automatic detection and mapping of ML concentrations in the coastal zone. Five convolutional neural networks (CNNs) were trained to classify UAS image tiles into two classes: (a) litter and (b) no litter. Testing the CCNs’ generalization ability to an unseen dataset, we found that the VVG19 CNN returned an overall accuracy of 77.6% and an f-score of 77.42%. ML density maps were created using the automated classification results. They were compared with those produced by a manual screening classification proving our approach’s geographical transferability to new and unknown beaches. Although ML recognition is still a challenging task, this study provides evidence about the feasibility of using a citizen science UAS-based monitoring method in combination with deep learning techniques for the quantification of the ML load in the coastal zone using density maps.

Download Full-text