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Application of Artificial Intelligence for Medical Research, 2nd Edition

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A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Molecular Biology".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 21252

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Special Issue Editor

Dr. Ryuji Hamamoto

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Guest Editor

National Cancer Center Research Institute, Tokyo, Japan
Interests: cancer epigenetics; precision medicine; machine learning; medical AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the enlightened postgenomic era, it is possible to obtain a large quantity of omics data, such as genome, epigenome, transcriptome and proteome data and medical images, with detailed clinical information. However, until recently, it was technically difficult to efficiently analyze enormous amounts of medical data in an integrated manner. Recent progress in artificial intelligence (AI) technology, which is mainly based on the development of machine learning and computer performance, has enabled the integrated analysis of medical big data. In particular, deep learning, a component of the broader family of machine learning methods based on learning data representations, is responsible for many of the new breakthroughs in AI, and it has already been reported that deep learning can outperform humans in many tasks. With this Special Issue, we aim to cover the application of artificial intelligence in medical research, with a particular focus on the integrated analysis of medical omics data using machine learning and deep learning. The articles presented in this Special Issue will include review manuscripts, research manuscripts, and short contributions.

Dr. Ryuji Hamamoto
Guest Editor

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Keywords

machine learning
deep learning
artificial intelligence
omics analysis
medical image analysis
big data

Published Papers (6 papers)

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Research

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18 pages, 964 KiB

Open AccessArticle

A Novel Knowledge Distillation-Based Feature Selection for the Classification of ADHD

by Naseer Ahmed Khan, Samer Abdulateef Waheeb, Atif Riaz and Xuequn Shang

Biomolecules 2021, 11(8), 1093; https://0-doi-org.brum.beds.ac.uk/10.3390/biom11081093 - 23 Jul 2021

Cited by 10 | Viewed by 2735

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a brain disorder with characteristics such as lack of concentration, excessive fidgeting, outbursts of emotions, lack of patience, difficulty in organizing tasks, increased forgetfulness, and interrupting conversation, and it is affecting millions of people worldwide. There is, until now, not a gold standard test using which an ADHD expert can differentiate between an individual with ADHD and a healthy subject, making accurate diagnosis of ADHD a challenging task. We are proposing a Knowledge Distillation-based approach to search for discriminating features between the ADHD and healthy subjects. Learned embeddings from a large neural network, trained on the functional connectivity features, were fed to one hidden layer Autoencoder for reproduction of the embeddings using the same connectivity features. Finally, a forward feature selection algorithm was used to select a combination of most discriminating features between the ADHD and the Healthy Controls. We achieved promising classification results for each of the five individual sites. A combined accuracy of 81% in KKI, 60% Peking, 56% in NYU, 64% NI, and 56% OHSU and individual site wise accuracy of 72% in KKI, 60% Peking, 73% in NYU, 70% NI, and 71% OHSU were obtained using our extracted features. Our results also outperformed state-of-the-art methods in literature which validates the efficacy of our proposed approach. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research, 2nd Edition)

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14 pages, 1556 KiB

Open AccessArticle

Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images

by Shintaro Sukegawa, Kazumasa Yoshii, Takeshi Hara, Tamamo Matsuyama, Katsusuke Yamashita, Keisuke Nakano, Kiyofumi Takabatake, Hotaka Kawai, Hitoshi Nagatsuka and Yoshihiko Furuki

Biomolecules 2021, 11(6), 815; https://0-doi-org.brum.beds.ac.uk/10.3390/biom11060815 - 30 May 2021

Cited by 35 | Viewed by 4530

Abstract

It is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care. Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and treatment stages from dental panoramic radiographic images. For objective labeling, 9767 dental implant images of 12 implant brands and treatment stages were obtained from the digital panoramic radiographs of patients who underwent procedures at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2020. Five deep convolutional neural network (CNN) models (ResNet18, 34, 50, 101 and 152) were evaluated. The accuracy, precision, recall, specificity, F1 score, and area under the curve score were calculated for each CNN. We also compared the multi-task and single-task accuracies of brand classification and implant treatment stage classification. Our analysis revealed that the larger the number of parameters and the deeper the network, the better the performance for both classifications. Multi-tasking significantly improved brand classification on all performance indicators, except recall, and significantly improved all metrics in treatment phase classification. Using CNNs conferred high validity in the classification of dental implant brands and treatment stages. Furthermore, multi-task learning facilitated analysis accuracy. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research, 2nd Edition)

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21 pages, 4311 KiB

Open AccessArticle

Imaging-Based Staging of Hepatic Fibrosis in Patients with Hepatitis B: A Dynamic Radiomics Model Based on Gd-EOB-DTPA-Enhanced MRI

by Rencheng Zheng, Chunzi Shi, Chengyan Wang, Nannan Shi, Tian Qiu, Weibo Chen, Yuxin Shi and He Wang

Biomolecules 2021, 11(2), 307; https://0-doi-org.brum.beds.ac.uk/10.3390/biom11020307 - 18 Feb 2021

Cited by 6 | Viewed by 2193

Abstract

Accurate grading of liver fibrosis can effectively assess the severity of liver disease and help doctors make an appropriate diagnosis. This study aimed to perform the automatic staging of hepatic fibrosis on patients with hepatitis B, who underwent gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging with dynamic radiomics analysis. The proposed dynamic radiomics model combined imaging features from multi-phase dynamic contrast-enhanced (DCE) images and time-domain information. Imaging features were extracted from the deep learning-based segmented liver volume, and time-domain features were further explored to analyze the variation in features during contrast enhancement. Model construction and evaluation were based on a 132-case data set. The proposed model achieved remarkable performance in significant fibrosis (fibrosis stage S1 vs. S2–S4; accuracy (ACC) = 0.875, area under the curve (AUC) = 0.867), advanced fibrosis (S1–S2 vs. S3–S4; ACC = 0.825, AUC = 0.874), and cirrhosis (S1–S3 vs. S4; ACC = 0.850, AUC = 0.900) classifications in the test set. It was more dominant compared with the conventional single-phase or multi-phase DCE-based radiomics models, normalized liver enhancement, and some serological indicators. Time-domain features were found to play an important role in the classification models. The dynamic radiomics model can be applied for highly accurate automatic hepatic fibrosis staging. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research, 2nd Edition)

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14 pages, 6718 KiB

Open AccessArticle

Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns

by Kaisa Liimatainen, Riku Huttunen, Leena Latonen and Pekka Ruusuvuori

Biomolecules 2021, 11(2), 264; https://0-doi-org.brum.beds.ac.uk/10.3390/biom11020264 - 11 Feb 2021

Cited by 16 | Viewed by 3274

Abstract

Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research, 2nd Edition)

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16 pages, 4480 KiB

Open AccessFeature PaperArticle

Model-Agnostic Method for Thoracic Wall Segmentation in Fetal Ultrasound Videos

by Kanto Shozu, Masaaki Komatsu, Akira Sakai, Reina Komatsu, Ai Dozen, Hidenori Machino, Suguru Yasutomi, Tatsuya Arakaki, Ken Asada, Syuzo Kaneko, Ryu Matsuoka, Akitoshi Nakashima, Akihiko Sekizawa and Ryuji Hamamoto

Biomolecules 2020, 10(12), 1691; https://0-doi-org.brum.beds.ac.uk/10.3390/biom10121691 - 17 Dec 2020

Cited by 28 | Viewed by 3732

Abstract

The application of segmentation methods to medical imaging has the potential to create novel diagnostic support models. With respect to fetal ultrasound, the thoracic wall is a key structure on the assessment of the chest region for examiners to recognize the relative orientation and size of structures inside the thorax, which are critical components in neonatal prognosis. In this study, to improve the segmentation performance of the thoracic wall in fetal ultrasound videos, we proposed a novel model-agnostic method using deep learning techniques: the Multi-Frame + Cylinder method (MFCY). The Multi-frame method (MF) uses time-series information of ultrasound videos, and the Cylinder method (CY) utilizes the shape of the thoracic wall. To evaluate the achieved improvement, we performed segmentation using five-fold cross-validation on 538 ultrasound frames in the four-chamber view (4CV) of 256 normal cases using U-net and DeepLabv3+. MFCY increased the mean values of the intersection over union (IoU) of thoracic wall segmentation from 0.448 to 0.493 for U-net and from 0.417 to 0.470 for DeepLabv3+. These results demonstrated that MFCY improved the segmentation performance of the thoracic wall in fetal ultrasound videos without altering the network structure. MFCY is expected to facilitate the development of diagnostic support models in fetal ultrasound by providing further accurate segmentation of the thoracic wall. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research, 2nd Edition)

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Review

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17 pages, 2415 KiB

Open AccessReview

A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning

by Satoshi Takahashi, Masamichi Takahashi, Shota Tanaka, Shunsaku Takayanagi, Hirokazu Takami, Erika Yamazawa, Shohei Nambu, Mototaka Miyake, Kaishi Satomi, Koichi Ichimura, Yoshitaka Narita and Ryuji Hamamoto

Biomolecules 2021, 11(4), 565; https://0-doi-org.brum.beds.ac.uk/10.3390/biom11040565 - 12 Apr 2021

Cited by 10 | Viewed by 3656

Abstract

Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research, 2nd Edition)

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