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Artificial Intelligence and Advanced Medical Imaging in Diagnosis and Precision Care

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A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Radiobiology and Nuclear Medicine".

Deadline for manuscript submissions: closed (7 July 2023) | Viewed by 12125

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Special Issue Editors

Dr. Binh P. Nguyen

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Guest Editor

School of Mathematics and Statistics, Victoria University of Wellington, Wellington 6012, New Zealand
Interests: machine learning; data science; deep learning; biomedical image analysis; health informatics; bioinformatics; drug discovery
Special Issues, Collections and Topics in MDPI journals

Dr. Trang Do

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Guest Editor

School of Innovation, Design and Technology, Wellington Institute of Technology, Wellington 5012, New Zealand
Interests: machine learning; deep learning; data visualization; health informatics; drug discovery; natural language processing; intelligent systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precision medicine will enable a patient's treatment pathway to be tailored to their individual characteristics. Using advances in artificial intelligence technologies, clinicians will be able to examine and interrogate only the most relevant data for each patient, enabling individualized treatments and achieving first-time-right diagnoses. Artificial intelligence and machine-learning technologies will bring data together to make precision medicine possible. Although we currently focus on a cure, precision care can enable more focus to be placed on the prevention of disease. In this context, patients will have more involvement and choice in their own care pathways. This Special Issue focuses on recent innovations and applications of artificial intelligence and advanced medical imaging techniques in diagnosis and precision care.

Dr. Binh P. Nguyen
Dr. Trang Do
Guest Editors

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Keywords

artificial intelligence
machine learning
deep learning
advanced imaging
radiomics
magnetic resonance imaging
computed tomography
spectral computed tomography
precision oncology
precision medicine
precision care
cancer diagnosis

Published Papers (5 papers)

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Research

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12 pages, 1590 KiB

Open AccessArticle

Machine Learning Identification of Obstructive Sleep Apnea Severity through the Patient Clinical Features: A Retrospective Study

by Antonino Maniaci, Paolo Marco Riela, Giannicola Iannella, Jerome Rene Lechien, Ignazio La Mantia, Marco De Vincentiis, Giovanni Cammaroto, Christian Calvo-Henriquez, Milena Di Luca, Carlos Chiesa Estomba, Alberto Maria Saibene, Isabella Pollicina, Giovanna Stilo, Paola Di Mauro, Angelo Cannavicci, Rodolfo Lugo, Giuseppe Magliulo, Antonio Greco, Annalisa Pace, Giuseppe Meccariello, Salvatore Cocuzza and Claudio Vicini Show full author list Hide full author list

Life 2023, 13(3), 702; https://doi.org/10.3390/life13030702 - 05 Mar 2023

Cited by 14 | Viewed by 2289

Abstract

Objectives: To evaluate the role of clinical scores assessing the risk of disease severity in patients with clinical suspicion of obstructive sleep apnea syndrome (OSA). The hypothesis was tested by applying artificial intelligence (AI) to demonstrate its effectiveness in distinguishing between mild–moderate OSA and severe OSA risk. Methods: A support vector machine model (SVM) was developed from the samples included in the analysis (N = 498), and they were split into 75% for training (N = 373) with the remaining for testing (N = 125). Two diagnostic thresholds were selected for OSA severity: mild to moderate (apnea–hypopnea index (AHI) ≥ 5 events/h and AHI < 30 events/h) and severe (AHI ≥ 30 events/h). The algorithms were trained and tested to predict OSA patient severity. Results: The sensitivity and specificity for the SVM model were 0.93 and 0.80 with an accuracy of 0.86; instead, the logistic regression full mode reported a value of 0.74 and 0.63, respectively, with an accuracy of 0.68. After backward stepwise elimination for features selection, the reduced logistic regression model demonstrated a sensitivity and specificity of 0.79 and 0.56, respectively, and an accuracy of 0.67. Conclusion: Artificial intelligence could be applied to patients with symptoms related to OSA to identify individuals with a severe OSA risk with clinical-based algorithms in the OSA framework. Full article

(This article belongs to the Special Issue Artificial Intelligence and Advanced Medical Imaging in Diagnosis and Precision Care)

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14 pages, 2195 KiB

Open AccessArticle

Accurate Kidney Pathological Image Classification Method Based on Deep Learning and Multi-Modal Fusion Method with Application to Membranous Nephropathy

by Fang Hao, Xueyu Liu, Ming Li and Weixia Han

Life 2023, 13(2), 399; https://doi.org/10.3390/life13020399 - 31 Jan 2023

Cited by 3 | Viewed by 1731

Abstract

Membranous nephropathy is one of the most prevalent conditions responsible for nephrotic syndrome in adults. It is clinically nonspecific and mainly diagnosed by kidney biopsy pathology, with three prevalent techniques: light microscopy, electron microscopy, and immunofluorescence microscopy. Manual observation of glomeruli one by one under the microscope is very time-consuming, and there are certain differences in the observation results between physicians. This study makes use of whole-slide images scanned by a light microscope as well as immunofluorescence images to classify patients with membranous nephropathy. The framework mainly includes a glomerular segmentation module, a confidence coefficient extraction module, and a multi-modal fusion module. This framework first identifies and segments the glomerulus from whole-slide images and immunofluorescence images, and then a glomerular classifier is trained to extract the features of each glomerulus. The results are then combined to produce the final diagnosis. The results of the experiments show that the F1-score of image classification results obtained by combining two kinds of features, which can reach 97.32%, is higher than those obtained by using only light-microscopy-observed images or immunofluorescent images, which reach 92.76% and 93.20%, respectively. Experiments demonstrate that considering both WSIs and immunofluorescence images is effective in improving the diagnosis of membranous nephropathy. Full article

(This article belongs to the Special Issue Artificial Intelligence and Advanced Medical Imaging in Diagnosis and Precision Care)

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11 pages, 4914 KiB

Open AccessArticle

Chest CT for Breast Cancer Diagnosis

by Elise Desperito, Lawrence Schwartz, Kathleen M. Capaccione, Brian T. Collins, Sachin Jamabawalikar, Boyu Peng, Rebecca Patrizio and Mary M. Salvatore

Life 2022, 12(11), 1699; https://doi.org/10.3390/life12111699 - 26 Oct 2022

Cited by 5 | Viewed by 4851

Abstract

Background: We report the results of our retrospective analysis of the ability of standard chest CT scans to correctly diagnose cancer in the breast. Methods: Four hundred and fifty-three consecutive women with chest CT scans (contrast and non-contrast) preceding mammograms within one year comprise the study population. All chest CT images were reviewed by an experienced fellowship-trained chest radiologist and mammograms by an experienced fellowship-trained mammographer without the benefit of prior or ancillary studies; only four mammographic views were included for analysis. The size, location, and shape of breast masses were documented; on CT, the average Hounsfield units were measured. On both imaging modalities, the presence of lymphadenopathy, architectural distortion, skin thickening, and microcalcifications were recorded. Ultimately, the interpreting radiologist was asked to decide if a biopsy was indicated, and these recommendations were correlated with the patient’s outcome. Findings: Nineteen of four hundred and fifty-three patients had breast cancer at the time of the mammography. Breast masses were the most common finding on chest CT, leading to the recommendation for biopsy. Hounsfield units were the most important feature for discerning benign from malignant masses. CT sensitivity, specificity, and accuracy of CT for breast cancer detection was 84.21%, 99.3%, and 98.68% compared to 78.95%, 93.78%, and 93.16% for four-view mammography. Chest CT scans with or without contrast had similar outcomes for specificity and accuracy, but sensitivity was slightly less without contrast. Chest CT alone, without the benefit of prior exams and patient recall, correctly diagnosed cancer with a p-value of <0.0001 compared to mammography with the same limitations. Conclusion: Chest CT accurately diagnosed breast cancer with few false positives and negatives and did so without the need for patient recall for additional imaging. Full article

(This article belongs to the Special Issue Artificial Intelligence and Advanced Medical Imaging in Diagnosis and Precision Care)

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Review

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13 pages, 1962 KiB

Open AccessReview

A Narrative Review of the Use of Artificial Intelligence in Breast, Lung, and Prostate Cancer

by Kishan Patel, Sherry Huang, Arnav Rashid, Bino Varghese and Ali Gholamrezanezhad

Life 2023, 13(10), 2011; https://doi.org/10.3390/life13102011 - 04 Oct 2023

Viewed by 1095

Abstract

Artificial intelligence (AI) has been an important topic within radiology. Currently, AI is used clinically to assist with the detection of lesions through detection systems. However, a number of recent studies have demonstrated the increased value of neural networks in radiology. With an increasing number of screening requirements for cancers, this review aims to study the accuracy of the numerous AI models used in the detection and diagnosis of breast, lung, and prostate cancers. This study summarizes pertinent findings from reviewed articles and provides analysis on the relevancy to clinical radiology. This study found that whereas AI is showing continual improvement in radiology, AI alone does not surpass the effectiveness of a radiologist. Additionally, it was found that there are multiple variations on how AI should be integrated with a radiologist’s workflow. Full article

(This article belongs to the Special Issue Artificial Intelligence and Advanced Medical Imaging in Diagnosis and Precision Care)

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30 pages, 3541 KiB

Open AccessReview

Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation

by Yiheng Lyu, Mohammed Bennamoun, Naeha Sharif, Gregory Y. H. Lip and Girish Dwivedi

Life 2023, 13(9), 1870; https://doi.org/10.3390/life13091870 - 05 Sep 2023

Viewed by 1151

Abstract

Atrial fibrillation arises mainly due to abnormalities in the cardiac conduction system and is associated with anatomical remodeling of the atria and the pulmonary veins. Cardiovascular imaging techniques, such as echocardiography, computed tomography, and magnetic resonance imaging, are crucial in the management of atrial fibrillation, as they not only provide anatomical context to evaluate structural alterations but also help in determining treatment strategies. However, interpreting these images requires significant human expertise. The potential of artificial intelligence in analyzing these images has been repeatedly suggested due to its ability to automate the process with precision comparable to human experts. This review summarizes the benefits of artificial intelligence in enhancing the clinical care of patients with atrial fibrillation through cardiovascular image analysis. It provides a detailed overview of the two most critical steps in image-guided AF management, namely, segmentation and classification. For segmentation, the state-of-the-art artificial intelligence methodologies and the factors influencing the segmentation performance are discussed. For classification, the applications of artificial intelligence in the diagnosis and prognosis of atrial fibrillation are provided. Finally, this review also scrutinizes the current challenges hindering the clinical applicability of these methods, with the aim of guiding future research toward more effective integration into clinical practice. Full article

(This article belongs to the Special Issue Artificial Intelligence and Advanced Medical Imaging in Diagnosis and Precision Care)

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