Towards AI-augmented radiology education: a web-based application for perception training in chest X-ray nodule detection

OBJECTIVES

Artificial intelligence (AI)-based applications for augmenting radiological education are underexplored. Prior studies have demonstrated the effectiveness of simulation in radiological perception training. This study aimed to develop and make available a pure web-based application called Perception Trainer for perception training in lung nodule detection in chest X-rays.

METHODS

Based on open-access data, we trained a deep-learning model for lung segmentation in chest X-rays. Subsequently, an algorithm for artificial lung nodule generation was implemented and combined with the segmentation model to allow on-the-fly procedural insertion of lung nodules in chest X-rays. This functionality was integrated into an existing zero-footprint web-based DICOM viewer, and a dynamic HTML page was created to specify case generation parameters.

RESULTS

The result is an easily accessible platform-agnostic web application available at: https://castlemountain.dk/mulrecon/perceptionTrainer.html.The application allows the user to specify the characteristics of lung nodules to be inserted into chest X-rays, and it produces automated feedback regarding nodule detection performance. Generated cases can be shared through a uniform resource locator.

CONCLUSION

We anticipate that the description and availability of our developed solution with open-sourced codes may help facilitate radiological education and stimulate the development of similar AI-augmented educational tools.

ADVANCES IN KNOWLEDGE

A web-based application applying AI-based techniques for radiological perception training was developed. The application demonstrates a novel approach for on-the-fly generation of cases in chest X-ray lung nodule detection employing deep-learning-based segmentation and lung nodule simulation.

Inter- and intra-observer variability of computed tomography-based parenchymal- and ductal diameters in chronic pancreatitis: a multi-observer international study

PURPOSE

The need for incorporation of quantitative imaging biomarkers of pancreatic parenchymal and ductal structures has been highlighted in recent proposals for new scoring systems in chronic pancreatitis (CP). To quantify inter- and intra-observer variability in CT-based measurements of ductal- and gland diameters in CP patients.

MATERIALS AND METHODS

Prospectively acquired pancreatic CT examinations from 50 CP patients were reviewed by 12 radiologists and four pancreatologists from 10 institutions. Assessment entailed measuring maximum diameter in the axial plane of four structures: (1) pancreatic head (PDhead), (2) pancreatic body (PDbody), (3) main pancreatic duct in the pancreatic head (MPDhead), and (4) body (MPDbody). Agreement was assessed by the 95% limits of agreement with the mean (LOAM), representing how much a single measurement for a specific subject may plausibly deviate from the mean of all measurements on the specific subject. Bland-Altman limits of agreement (LoA) were generated for intra-observer pairs.

RESULTS

The 16 observers completed 6400 caliper placements comprising a first and second measurement session. The widest inter-observer LOAM was seen with PDhead (± 9.1 mm), followed by PDbody (± 5.1 mm), MPDhead (± 3.2 mm), and MPDbody (± 2.6 mm), whereas the mean intra-observer LoA width was ± 7.3, ± 5.1, ± 3.7, and ± 2.4 mm, respectively.

CONCLUSION

Substantial intra- and inter-observer variability was observed in pancreatic two-point measurements. This was especially pronounced for parenchymal and duct diameters of the pancreatic head. These findings challenge the implementation of two-point measurements as the foundation for quantitative imaging scoring systems in CP.

Ultra-low-dose non-contrast CT and CT angiography can be used interchangeably for assessing maximal abdominal aortic diameter

Background

Routine CT scans may increasingly be used to document normal aortic size and to detect incidental abdominal aortic aneurysms.

Purpose

To determine whether ultra-low-dose non-contrast CT (ULDNC-CT) can be used instead of the gold standard CT angiography (CTA) for assessment of maximal abdominal aortic diameter.

Materials and Methods

This retrospective study included 50 patients who underwent CTA and a normal-dose non-contrast CT for suspected renal artery stenosis. ULDNC-CT datasets were generated from the normal-dose non-contrast CT datasets using a simulation technique. Using the centerline technique, radiology consultants (n = 4) and residents (n = 3) determined maximal abdominal aortic diameter. The limits of agreement with the mean (LOAM) was used to access observer agreement. LOAM represents how much a measurement by a single observer may plausibly deviate from the mean of all observers on the specific subject.

Results

Observers completed 1400 measurements encompassing repeated CTA and ULDNC-CT measurements. The mean diameter was 24.0 and 25.0 mm for CTA and ULDNC-CT, respectively, yielding a significant but minor mean difference of 1.0 mm. The 95% LOAM reproducibility was similar for CTA and ULDNC-CT (2.3 vs 2.3 mm). In addition, the 95% LOAM and mean diameters were similar for CTA and ULDNC-CT when observers were grouped as consultants and residents.

Conclusions

Ultra-low-dose non-contrast CT exhibited similar accuracy and reproducibility of measurements compared with CTA for assessing maximal abdominal aortic diameter supporting that ULDNC-CT can be used interchangeably with CTA in the lower range of aortic sizes.

Web-Based GPU-Accelerated Application for Multiplanar Reconstructions from Conventional 2D Ultrasound

PURPOSE

 In ultrasound education there is a need for interactive web-based learning resources. The purpose of this project was to develop a web-based application that enables the generation and exploration of volumetric datasets from cine loops obtained with conventional 2D ultrasound.

MATERIALS AND METHODS

 JavaScript code for ultrasound video loading and the generation of volumetric datasets was created and merged with an existing web-based imaging viewer based on JavaScript and HTML5. The Web Graphics Library was utilized to enable hardware-accelerated image rendering.

RESULTS

 The result is a web application that works in most major browsers without any plug-ins. It allows users to load a conventional 2D ultrasound cine loop which can subsequently be manipulated with on-the-fly multiplanar reconstructions as in a Digital Imaging and Communications in Medicine (DICOM) viewer. The application is freely accessible at (http://www.castlemountain.dk/atlas/index.php?page=mulrecon&mulreconPage=sonoviewer) where a demonstration of web-based sharing of generated cases can also be found.

CONCLUSION

 The developed web-based application is unique in its ability to easily perform loading of one's own ultrasound clips and conduct multiplanar reconstructions where interactive cases can be shared on the Internet.

MR and ultrasound cardiac image dynamic visualization and synchronization over Internet for distributed heart function diagnosis

Dual-modality 4D cardiac data visualization can convey a significant amount of complementary image information from various sources into a single and meaningful display. Even though there are existing publications on combining multiple medical images into a unique representation, there has been no work on rendering a series of cardiac image sequences, acquired from multiple sources, using web browsers and synchronizing the result over the Internet in real time. The ability to display multi-modality beating heart images using Web-based technology is hampered by the lack of efficient algorithms for fusing and visualizing constantly updated multi-source images and streaming the rendering results using internet protocols. To address this practical issue, in this paper we introduce a new Internet-based algorithm and a software platform running on a Node.js server, where a series of registered cardiac images from both magnetic resonance (MR) and ultrasound are employed to display dynamic fused cardiac structures in web browsers. Taking advantage of the bidirectional WebSocket protocol and WebGL-based graphics acceleration, internal cardiac structures are dynamically displayed, and the results of rendering and data exploration are synchronized among all the connected client computers. The presented research and software have the potential to provide clinicians with comprehensive information and intuitive feedback relating to cardiac behavior and anatomy and could impact areas such as distributed diagnosis of cardiac function and collaborative treatment planning for various heart diseases.

Web-based imaging viewer for real-color volumetric reconstruction of human visible project and DICOM datasets

Anatomy remains a cornerstone of medical education. It is vital that students achieve a robust understanding of the spatial relationships between anatomical structures in three dimensions. Volumetric medical imaging studies and true-color cryosectional three-dimensional images of visible human datasets are useful for enhancing anatomy education. However, the software systems available for viewing these datasets have important limitations. A web-based application called Mulrecon Color, which can overcome a number of those limitations, is introduced. Mulrecon Color enables volumetric medical and full color cryosectional datasets to be explored without requiring installation, and can therefore be used on a broad range of desktop, mobile, and even virtual reality devices. The web-based application has an interface that resembles a DICOM viewer used in radiological practice, and can be used both in anatomical labs and off campus for self-study. The Mulrecon Color application is released as an open source tool. It can be retrieved at a project website where sample datasets are also available.

Web-Based Technology for Remote Viewing of Radiological Images: App Validation

BACKGROUND

Internet technologies can create advanced and rich web-based apps that allow radiologists to easily access teleradiology systems and remotely view medical images. However, each technology has its own drawbacks. It is difficult to balance the advantages and disadvantages of these internet technologies and identify an optimal solution for the development of medical imaging apps.

OBJECTIVE

This study aimed to compare different internet platform technologies for remotely viewing radiological images and analyze their advantages and disadvantages.

METHODS

Oracle Java, Adobe Flash, and HTML5 were each used to develop a comprehensive web-based medical imaging app that connected to a medical image server and provided several required functions for radiological interpretation (eg, navigation, magnification, windowing, and fly-through). Java-, Flash-, and HTML5-based medical imaging apps were tested on different operating systems over a local area network and a wide area network. Three computed tomography colonography data sets and 2 ordinary personal computers were used in the experiment.

RESULTS

The experimental results demonstrated that Java-, Flash-, and HTML5-based apps had the ability to provide real-time 2D functions. However, for 3D, performances differed between the 3 apps. The Java-based app had the highest frame rate of volume rendering. However, it required the longest time for surface rendering and failed to run surface rendering in macOS. The HTML5-based app had the fastest surface rendering and the highest speed for fly-through without platform dependence. Volume rendering, surface rendering, and fly-through performances of the Flash-based app were significantly worse than those of the other 2 apps.

CONCLUSIONS

Oracle Java, Adobe Flash, and HTML5 have individual strengths in the development of remote access medical imaging apps. However, HTML5 is a promising technology for remote viewing of radiological images and can provide excellent performance without requiring any plug-ins.