Advances in Artificial Intelligence for Medical Image Analysis

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

Deadline for manuscript submissions: 30 October 2024 | Viewed by 3098

Special Issue Editor

School of Software, Yunnan University, Kunming, China
Interests: machine learning; artificial neural networks; image processing; bioinformatics; artificial-intelligence-based information security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical image analysis plays a vital role in diagnosing numerous pathologies, ranging from infectious diseases to cancer. Additionally, artificial intelligence (AI) is rapidly transforming the field of medical imaging, with new AI-powered tools and techniques being developed all the time. These advances are making it possible to detect diseases earlier, more accurately, and more efficiently than ever before.

One of the most promising areas of AI in medical imaging is in the development of deep learning algorithms. Deep learning has powerful capabilities of feature extraction and pattern classification, and it can learn knowledge from datasets. This makes it well-suited for tasks such as image classification, object detection, and segmentation, which are all important for medical image analysis. Consequently, the processing burden in medical imaging has now shifted from the human to the computer side, thus allowing more researchers to step into this well-regarded and momentous area.  These advances are making it possible to improve the quality of care for patients and to save lives.

This Special Issue seeks high-quality research articles or comprehensive reviews with the applications of Artificial intelligence in Medical Image Analysis.

Dr. Xin Jin
Guest Editor

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • medical imaging
  • deep learning for medical imaging
  • histopathological image analysis
  • medical diagnosis
  • visualization in biomedical imaging
  • magnetic resonance imaging (MRI)
  • X-ray computed tomography (CT)
  • positron emission tomography (PET)
  • medical image fusion

Published Papers (3 papers)

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Research

7 pages, 500 KiB  
Article
Assessment of Bone Age Based on Hand Radiographs Using Regression-Based Multi-Modal Deep Learning
by Jeoung Kun Kim, Donghwi Park and Min Cheol Chang
Life 2024, 14(6), 774; https://doi.org/10.3390/life14060774 (registering DOI) - 18 Jun 2024
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Abstract
(1) Objective: In this study, a regression-based multi-modal deep learning model was developed for use in bone age assessment (BAA) utilizing hand radiographic images and clinical data, including patient gender and chronological age, as input data. (2) Methods: A dataset of hand radiographic [...] Read more.
(1) Objective: In this study, a regression-based multi-modal deep learning model was developed for use in bone age assessment (BAA) utilizing hand radiographic images and clinical data, including patient gender and chronological age, as input data. (2) Methods: A dataset of hand radiographic images from 2974 pediatric patients was used to develop a regression-based multi-modal BAA model. This model integrates hand radiographs using EfficientNetV2S convolutional neural networks (CNNs) and clinical data (gender and chronological age) processed by a simple deep neural network (DNN). This approach enhances the model’s robustness and diagnostic precision, addressing challenges related to imbalanced data distribution and limited sample sizes. (3) Results: The model exhibited good performance on BAA, with an overall mean absolute error (MAE) of 0.410, root mean square error (RMSE) of 0.637, and accuracy of 91.1%. Subgroup analysis revealed higher accuracy in females ≤ 11 years (MAE: 0.267, RMSE: 0.453, accuracy: 95.0%) and >11 years (MAE: 0.402, RMSE: 0.634, accuracy 92.4%) compared to males ≤ 13 years (MAE: 0.665, RMSE: 0.912, accuracy: 79.7%) and >13 years (MAE: 0.647, RMSE: 1.302, accuracy: 84.6%). (4) Conclusion: This model showed a generally good performance on BAA, showing a better performance in female pediatrics compared to male pediatrics and an especially robust performance in female pediatrics ≤ 11 years. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Medical Image Analysis)
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20 pages, 10775 KiB  
Article
Generative-Adversarial-Network-Based Image Reconstruction for the Capacitively Coupled Electrical Impedance Tomography of Stroke
by Mikhail Ivanenko, Damian Wanta, Waldemar T. Smolik, Przemysław Wróblewski and Mateusz Midura
Life 2024, 14(3), 419; https://doi.org/10.3390/life14030419 - 21 Mar 2024
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Abstract
This study investigated the potential of machine-learning-based stroke image reconstruction in capacitively coupled electrical impedance tomography. The quality of brain images reconstructed using the adversarial neural network (cGAN) was examined. The big data required for supervised network training were generated using a two-dimensional [...] Read more.
This study investigated the potential of machine-learning-based stroke image reconstruction in capacitively coupled electrical impedance tomography. The quality of brain images reconstructed using the adversarial neural network (cGAN) was examined. The big data required for supervised network training were generated using a two-dimensional numerical simulation. The phantom of an axial cross-section of the head without and with impact lesions was an average of a three-centimeter-thick layer corresponding to the height of the sensing electrodes. Stroke was modeled using regions with characteristic electrical parameters for tissues with reduced perfusion. The head phantom included skin, skull bone, white matter, gray matter, and cerebrospinal fluid. The coupling capacitance was taken into account in the 16-electrode capacitive sensor model. A dedicated ECTsim toolkit for Matlab was used to solve the forward problem and simulate measurements. A conditional generative adversarial network (cGAN) was trained using a numerically generated dataset containing samples corresponding to healthy patients and patients affected by either hemorrhagic or ischemic stroke. The validation showed that the quality of images obtained using supervised learning and cGAN was promising. It is possible to visually distinguish when the image corresponds to the patient affected by stroke, and changes caused by hemorrhagic stroke are the most visible. The continuation of work towards image reconstruction for measurements of physical phantoms is justified. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Medical Image Analysis)
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14 pages, 4374 KiB  
Article
Deep Learning Super-Resolution Technique Based on Magnetic Resonance Imaging for Application of Image-Guided Diagnosis and Surgery of Trigeminal Neuralgia
by Jun Ho Hwang, Chang Kyu Park, Seok Bin Kang, Man Kyu Choi and Won Hee Lee
Life 2024, 14(3), 355; https://doi.org/10.3390/life14030355 - 7 Mar 2024
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Abstract
This study aimed to implement a deep learning-based super-resolution (SR) technique that can assist in the diagnosis and surgery of trigeminal neuralgia (TN) using magnetic resonance imaging (MRI). Experimental methods applied SR to MRI data examined using five techniques, including T2-weighted imaging (T2WI), [...] Read more.
This study aimed to implement a deep learning-based super-resolution (SR) technique that can assist in the diagnosis and surgery of trigeminal neuralgia (TN) using magnetic resonance imaging (MRI). Experimental methods applied SR to MRI data examined using five techniques, including T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), contrast-enhancement T1WI (CE-T1WI), T2WI turbo spin–echo series volume isotropic turbo spin–echo acquisition (VISTA), and proton density (PD), in patients diagnosed with TN. The image quality was evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). High-quality reconstructed MRI images were assessed using the Leksell coordinate system in gamma knife radiosurgery (GKRS). The results showed that the PSNR and SSIM values achieved by SR were higher than those obtained by image postprocessing techniques, and the coordinates of the images reconstructed in the gamma plan showed no differences from those of the original images. Consequently, SR demonstrated remarkable effects in improving the image quality without discrepancies in the coordinate system, confirming its potential as a useful tool for the diagnosis and surgery of TN. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Medical Image Analysis)
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