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Special Issue "Artificial Intelligence in Radiology 2.0"

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 30 November 2023 | Viewed by 5868

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Special Issue Editors

Dr. Xuan V. Nguyen

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Guest Editor

Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
Interests: diagnostic radiology; neuroradiology; machine learning; quantitative modeling

Dr. Engin Dikici

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Guest Editor

Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
Interests: computer vision; convolutional neural networks; generative adversarial network; self- and semi-supervised learning strategies

Special Issue Information

Dear Colleagues,

Advances in computer vision over the past decade have led to a growing interest in machine learning and other artificial intelligence (AI) applications in radiology. While a small but growing number of AI software programs have been approved for clinical use, there are numerous potential uses of AI in radiology that are areas of active investigation. Among the AI processes relevant to radiologic image interpretation is computer-assisted detection or diagnosis, utilizing deep convolutional neural networks and other state-of-the-art AI methodologies to automate such computer vision tasks as image classification, object detection/localization, and image segmentation. Prognostication or clinical decision-making could also be assisted by the AI-facilitated assessment of images and/or other clinical data. There are also potential roles of AI in radiology beyond image interpretation, such as clinical decision support, protocol selection, improving the image acquisition speed or quality, reporting and communication, and other clinical or research workflow processes. We include discussions of the repertoire of available network architectures applicable to radiology, including deep-learning convolutional neural networks commonly employed for image classification and other architectures such as generative adversarial networks and U-Net-based architectures. The aims of this Special Issue are to (1) summarize current research on several broad categories of AI tasks relevant to radiology through a series of multi-disciplinary literature reviews and (2) illustrate AI applications in selected radiology workflows through a diverse set of original research articles.

Dr. Xuan V. Nguyen
Dr. Engin Dikici
Guest Editors

Manuscript Submission Information

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Keywords

radiology workflow
computer-assisted diagnosis
computer-assisted detection
machine learning
deep learning
convolutional neural networks
medical diagnosis
diagnostic radiology

Published Papers (4 papers)

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Research

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Open AccessArticle

Deep-Learning-Based Segmentation of the Shoulder from MRI with Inference Accuracy Prediction

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Diagnostics 2023, 13(10), 1668; https://doi.org/10.3390/diagnostics13101668 - 09 May 2023

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Abstract

Three-dimensional (3D)-image-based anatomical analysis of rotator cuff tear patients has been proposed as a way to improve repair prognosis analysis to reduce the incidence of postoperative retear. However, for application in clinics, an efficient and robust method for the segmentation of anatomy from MRI is required. We present the use of a deep learning network for automatic segmentation of the humerus, scapula, and rotator cuff muscles with integrated automatic result verification. Trained on N = 111 and tested on N = 60 diagnostic T1-weighted MRI of 76 rotator cuff tear patients acquired from 19 centers, a nnU-Net segmented the anatomy with an average Dice coefficient of 0.91 ± 0.06. For the automatic identification of inaccurate segmentations during the inference procedure, the nnU-Net framework was adapted to allow for the estimation of label-specific network uncertainty directly from its subnetworks. The average Dice coefficient of segmentation results from the subnetworks identified labels requiring segmentation correction with an average sensitivity of 1.0 and a specificity of 0.94. The presented automatic methods facilitate the use of 3D diagnosis in clinical routine by eliminating the need for time-consuming manual segmentation and slice-by-slice segmentation verification. Full article

(This article belongs to the Special Issue Artificial Intelligence in Radiology 2.0)

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Open AccessArticle

Advancing Brain Metastases Detection in T1-Weighted Contrast-Enhanced 3D MRI Using Noisy Student-Based Training

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Luciano M. Prevedello

Diagnostics 2022, 12(8), 2023; https://doi.org/10.3390/diagnostics12082023 - 21 Aug 2022

Cited by 1 | Viewed by 1021

Abstract

The detection of brain metastases (BM) in their early stages could have a positive impact on the outcome of cancer patients. The authors previously developed a framework for detecting small BM (with diameters of <15 mm) in T1-weighted contrast-enhanced 3D magnetic resonance images (T1c). This study aimed to advance the framework with a noisy-student-based self-training strategy to use a large corpus of unlabeled T1c data. Accordingly, a sensitivity-based noisy-student learning approach was formulated to provide high BM detection sensitivity with a reduced count of false positives. This paper (1) proposes student/teacher convolutional neural network architectures, (2) presents data and model noising mechanisms, and (3) introduces a novel pseudo-labeling strategy factoring in the sensitivity constraint. The evaluation was performed using 217 labeled and 1247 unlabeled exams via two-fold cross-validation. The framework utilizing only the labeled exams produced 9.23 false positives for 90% BM detection sensitivity, whereas the one using the introduced learning strategy led to ~9% reduction in false detections (i.e., 8.44). Significant reductions in false positives (>10%) were also observed in reduced labeled data scenarios (using 50% and 75% of labeled data). The results suggest that the introduced strategy could be utilized in existing medical detection applications with access to unlabeled datasets to elevate their performances. Full article

(This article belongs to the Special Issue Artificial Intelligence in Radiology 2.0)

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Review

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Artificial Intelligence, Augmented Reality, and Virtual Reality Advances and Applications in Interventional Radiology

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Diagnostics 2023, 13(5), 892; https://doi.org/10.3390/diagnostics13050892 - 27 Feb 2023

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Abstract

Artificial intelligence (AI) uses computer algorithms to process and interpret data as well as perform tasks, while continuously redefining itself. Machine learning, a subset of AI, is based on reverse training in which evaluation and extraction of data occur from exposure to labeled examples. AI is capable of using neural networks to extract more complex, high-level data, even from unlabeled data sets, and better emulate, or even exceed, the human brain. Advances in AI have and will continue to revolutionize medicine, especially the field of radiology. Compared to the field of interventional radiology, AI innovations in the field of diagnostic radiology are more widely understood and used, although still with significant potential and growth on the horizon. Additionally, AI is closely related and often incorporated into the technology and programming of augmented reality, virtual reality, and radiogenomic innovations which have the potential to enhance the efficiency and accuracy of radiological diagnoses and treatment planning. There are many barriers that limit the applications of artificial intelligence applications into the clinical practice and dynamic procedures of interventional radiology. Despite these barriers to implementation, artificial intelligence in IR continues to advance and the continued development of machine learning and deep learning places interventional radiology in a unique position for exponential growth. This review describes the current and possible future applications of artificial intelligence, radiogenomics, and augmented and virtual reality in interventional radiology while also describing the challenges and limitations that must be addressed before these applications can be fully implemented into common clinical practice. Full article

(This article belongs to the Special Issue Artificial Intelligence in Radiology 2.0)

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Open AccessReview

Artificial Intelligence in Emergency Radiology: Where Are We Going?

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Gianpaolo Carrafiello

Diagnostics 2022, 12(12), 3223; https://doi.org/10.3390/diagnostics12123223 - 19 Dec 2022

Cited by 2 | Viewed by 1557

Abstract

Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and management of different pathologies is essential to saving patients’ lives. Artificial Intelligence (AI) has many potential applications in emergency radiology: firstly, image acquisition can be facilitated by reducing acquisition times through automatic positioning and minimizing artifacts with AI-based reconstruction systems to optimize image quality, even in critical patients; secondly, it enables an efficient workflow (AI algorithms integrated with RIS–PACS workflow), by analyzing the characteristics and images of patients, detecting high-priority examinations and patients with emergent critical findings. Different machine and deep learning algorithms have been trained for the automated detection of different types of emergency disorders (e.g., intracranial hemorrhage, bone fractures, pneumonia), to help radiologists to detect relevant findings. AI-based smart reporting, summarizing patients’ clinical data, and analyzing the grading of the imaging abnormalities, can provide an objective indicator of the disease’s severity, resulting in quick and optimized treatment planning. In this review, we provide an overview of the different AI tools available in emergency radiology, to keep radiologists up to date on the current technological evolution in this field. Full article

(This article belongs to the Special Issue Artificial Intelligence in Radiology 2.0)

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