Artificial Intelligence in Medical Imaging and Image Processing

Topic Information

Dear Colleagues,

In modern healthcare, the importance of computer-aided diagnosis is quickly becoming obvious, with clear benefits for the medical professionals and patients. Automatization of processes traditionally maintained by human professionals is also growing in importance. The process of image analysis can be supported by the use of networks that can carry out multilayer analyses of patterns—collectively called artificial intelligence (AI). If supported by large datasets of input data, computer networks can suggest the result with low error bias. Medical imaging focused on pattern detection is typically supported by AI algorithms. AI can be used as an important aid in three major steps of decision making in the medical imaging workflow: detection (image segmentation), recognition (assignment to the class), and result description (transformation of the result to natural language). The implementation of AI algorithms may participate in the diagnostic process standardization and markedly reduces the time needed to achieve pathology detection and description of the results. With AI support, medical specialists may work more effectively, which can improve healthcare quality. As AI has been a topic of interest for a while now, there are many approaches to and techniques for the implementation of AI based on different computing methods designed to work in various systems. The aim of this Special Issue in to present the current knowledge dedicated to the AI methods used in medical systems, with their applications in different fields of diagnostic imaging. Our goal is for this collection of works to contribute to the exchange of knowledge resulting in a better understanding of AI technical aspects and applications in modern radiology.

Dr. Rafał Obuchowicz
Prof. Dr. Michał Strzelecki
Prof. Dr. Adam Piorkowski
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
BioMed biomed	-	-	2021	24.6 Days	1000 CHF	Submit
Cancers cancers	6.575	5.8	2009	17.4 Days	2600 CHF	Submit
Diagnostics diagnostics	3.992	2.4	2011	17.7 Days	2000 CHF	Submit
Journal of Clinical Medicine jcm	4.964	4.4	2012	18 Days	2600 CHF	Submit
Tomography tomography	3.000	3.5	2015	24.7 Days	1800 CHF	Submit

Published Papers (2 papers)

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All BioMed Cancers Diagnostics JCM Tomography

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Open AccessArticle

COVID and Cancer: A Complete 3D Advanced Radiological CT-Based Analysis to Predict the Outcome

by

Syed Rahmanuddin

,

Asma Jamil

,

Ammar Chaudhry

,

Tyler Seto

,

Jordyn Brase

,

Pejman Motarjem

,

Marjaan Khan

,

Cristian Tomasetti

,

Umme Farwa

,

William Boswell

,

Haris Ali

,

Danielle Guidaben

,

Rafay Haseeb

,

Guibo Luo

,

Guido Marcucci

,

Steven T. Rosen

and

Wenli Cai

Cancers 2023, 15(3), 651; https://doi.org/10.3390/cancers15030651 - 20 Jan 2023

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Abstract

Background: Cancer patients infected with COVID-19 were shown in a multitude of studies to have poor outcomes on the basis of older age and weak immune systems from cancer as well as chemotherapy. In this study, the CT examinations of 22 confirmed COVID-19 [...] Read more.

Background: Cancer patients infected with COVID-19 were shown in a multitude of studies to have poor outcomes on the basis of older age and weak immune systems from cancer as well as chemotherapy. In this study, the CT examinations of 22 confirmed COVID-19 cancer patients were analyzed. Methodology: A retrospective analysis was conducted on 28 cancer patients, of which 22 patients were COVID positive. The CT scan changes before and after treatment and the extent of structural damage to the lungs after COVID-19 infection was analyzed. Structural damage to a lung was indicated by a change in density measured in Hounsfield units (HUs) and by lung volume reduction. A 3D radiometric analysis was also performed and lung and lesion histograms were compared. Results: A total of 22 cancer patients were diagnosed with COVID-19 infection. A repeat CT scan were performed in 15 patients after they recovered from infection. Most of the study patients were diagnosed with leukemia. A secondary clinical analysis was performed to show the associations of COVID treatment on the study subjects, lab data, and outcome on mortality. It was found that post COVID there was a decrease of >50% in lung volume and a higher density in the form of HUs due to scar tissue formation post infection. Conclusion: It was concluded that COVID-19 infection may have further detrimental effects on the lungs of cancer patients, thereby, decreasing their lung volume and increasing their lung density due to scar formation. Full article

(This article belongs to the Topic Artificial Intelligence in Medical Imaging and Image Processing)

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Open AccessSystematic Review

Deep Learning for Detecting Brain Metastases on MRI: A Systematic Review and Meta-Analysis

by

Burak B. Ozkara

,

Melissa M. Chen

,

Christian Federau

,

Mert Karabacak

,

Tina M. Briere

,

Jing Li

and

Max Wintermark

Cancers 2023, 15(2), 334; https://doi.org/10.3390/cancers15020334 - 04 Jan 2023

Viewed by 534

Abstract

Since manual detection of brain metastases (BMs) is time consuming, studies have been conducted to automate this process using deep learning. The purpose of this study was to conduct a systematic review and meta-analysis of the performance of deep learning models that use [...] Read more.

Since manual detection of brain metastases (BMs) is time consuming, studies have been conducted to automate this process using deep learning. The purpose of this study was to conduct a systematic review and meta-analysis of the performance of deep learning models that use magnetic resonance imaging (MRI) to detect BMs in cancer patients. A systematic search of MEDLINE, EMBASE, and Web of Science was conducted until 30 September 2022. Inclusion criteria were: patients with BMs; deep learning using MRI images was applied to detect the BMs; sufficient data were present in terms of detective performance; original research articles. Exclusion criteria were: reviews, letters, guidelines, editorials, or errata; case reports or series with less than 20 patients; studies with overlapping cohorts; insufficient data in terms of detective performance; machine learning was used to detect BMs; articles not written in English. Quality Assessment of Diagnostic Accuracy Studies-2 and Checklist for Artificial Intelligence in Medical Imaging was used to assess the quality. Finally, 24 eligible studies were identified for the quantitative analysis. The pooled proportion of patient-wise and lesion-wise detectability was 89%. Articles should adhere to the checklists more strictly. Deep learning algorithms effectively detect BMs. Pooled analysis of false positive rates could not be estimated due to reporting differences. Full article

(This article belongs to the Topic Artificial Intelligence in Medical Imaging and Image Processing)

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