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Biomedicines
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Research and Application of Artificial Intelligence in Assisting Disease Diagnosis...
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Research and Application of Artificial Intelligence in Assisting Disease Diagnosis and Treatment

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular and Translational Medicine".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 4278

Special Issue Editor

Dr. Jia Wu

E-Mail Website
Guest Editor
1. Research Center for Artificial Intelligence, Monash University, Melbourne, Clayton, VIC 3800, Australia
2. School of Computer Science and Engineering, Central South University, Changsha 410083, China
Interests: medical informatics; big data research; wireless network; decision-making system; machine learning; knowledge management; computational intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Clinically, high-quality diagnosis is a prerequisite for effective treatment. However, the current state of healthcare is that the growth rate of medical resources and manpower cannot meet the demand for medical diagnosis. Medical Assisted Diagnosis System aims to process patient information through existing technology and combine it with computer analysis methods to assist and provide reference from all stages of physician diagnosis. It has a wide range of contents, including information collection, processing, judgment, and conclusion. The essence of the system is to give an effective analysis and evaluation of the patient's illness through a deep fusion of technologies such as information retrieval, automatic reasoning, natural language processing, and medical image processing. This type of method can greatly improve the accuracy and efficiency of diagnosis. The purpose of this special issue is to bring together original research and review papers that discuss new findings on how artificial intelligence techniques can solve problems related to intelligent assisted diagnostic systems.

Dr. Jia Wu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • disease diagnosis
  • computer science
  • interdisciplinary applications

Published Papers (3 papers)

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Research

26 pages, 5892 KiB  
Article
An Innovative Solution Based on TSCA-ViT for Osteosarcoma Diagnosis in Resource-Limited Settings
by Zengxiao He, Jun Liu, Fangfang Gou and Jia Wu
Biomedicines 2023, 11(10), 2740; https://doi.org/10.3390/biomedicines11102740 - 10 Oct 2023
Cited by 1 | Viewed by 893
Abstract
Identifying and managing osteosarcoma pose significant challenges, especially in resource-constrained developing nations. Advanced diagnostic methods involve isolating the nucleus from cancer cells for comprehensive analysis. However, two main challenges persist: mitigating image noise during the capture and transmission of cellular sections, and providing [...] Read more.
Identifying and managing osteosarcoma pose significant challenges, especially in resource-constrained developing nations. Advanced diagnostic methods involve isolating the nucleus from cancer cells for comprehensive analysis. However, two main challenges persist: mitigating image noise during the capture and transmission of cellular sections, and providing an efficient, accurate, and cost-effective solution for cell nucleus segmentation. To tackle these issues, we introduce the Twin-Self and Cross-Attention Vision Transformer (TSCA-ViT). This pioneering AI-based system employs a directed filtering algorithm for noise reduction and features an innovative transformer architecture with a twin attention mechanism for effective segmentation. The model also incorporates cross-attention-enabled skip connections to augment spatial information. We evaluated our method on a dataset of 1000 osteosarcoma pathology slide images from the Second People’s Hospital of Huaihua, achieving a remarkable average precision of 97.7%. This performance surpasses traditional methodologies. Furthermore, TSCA-ViT offers enhanced computational efficiency owing to its fewer parameters, which results in reduced time and equipment costs. These findings underscore the superior efficacy and efficiency of TSCA-ViT, offering a promising approach for addressing the ongoing challenges in osteosarcoma diagnosis and treatment, particularly in settings with limited resources. Full article
(This article belongs to the Special Issue Research and Application of Artificial Intelligence in Assisting Disease Diagnosis and Treatment)
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11 pages, 1437 KiB  
Article
Prediction of Visual Acuity in Pathologic Myopia with Myopic Choroidal Neovascularization Treated with Anti-Vascular Endothelial Growth Factor Using a Deep Neural Network Based on Optical Coherence Tomography Images
by Migyeong Yang, Jinyoung Han, Ji In Park, Joon Seo Hwang, Jeong Mo Han, Jeewoo Yoon, Seong Choi, Gyudeok Hwang and Daniel Duck-Jin Hwang
Biomedicines 2023, 11(8), 2238; https://doi.org/10.3390/biomedicines11082238 - 09 Aug 2023
Cited by 1 | Viewed by 1068
Abstract
Myopic choroidal neovascularization (mCNV) is a common cause of vision loss in patients with pathological myopia. However, predicting the visual prognosis of patients with mCNV remains challenging. This study aimed to develop an artificial intelligence (AI) model to predict visual acuity (VA) in [...] Read more.
Myopic choroidal neovascularization (mCNV) is a common cause of vision loss in patients with pathological myopia. However, predicting the visual prognosis of patients with mCNV remains challenging. This study aimed to develop an artificial intelligence (AI) model to predict visual acuity (VA) in patients with mCNV. This study included 279 patients with mCNV at baseline; patient data were collected, including optical coherence tomography (OCT) images, VA, and demographic information. Two models were developed: one comprising horizontal/vertical OCT images (H/V cuts) and the second comprising 25 volume scan images. The coefficient of determination (R2) and root mean square error (RMSE) were computed to evaluate the performance of the trained network. The models achieved high performance in predicting VA after 1 (R2 = 0.911, RMSE = 0.151), 2 (R2 = 0.894, RMSE = 0.254), and 3 (R2 = 0.891, RMSE = 0.227) years. Using multiple-volume scanning, OCT images enhanced the performance of the models relative to using only H/V cuts. This study proposes AI models to predict VA in patients with mCNV. The models achieved high performance by incorporating the baseline VA, OCT images, and post-injection data. This model could assist in predicting the visual prognosis and evaluating treatment outcomes in patients with mCNV undergoing intravitreal anti-vascular endothelial growth factor therapy. Full article
(This article belongs to the Special Issue Research and Application of Artificial Intelligence in Assisting Disease Diagnosis and Treatment)
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16 pages, 5667 KiB  
Article
A Deep Learning Approach for Prognostic Evaluation of Lung Adenocarcinoma Based on Cuproptosis-Related Genes
by Pengchen Liang, Jianguo Chen, Lei Yao, Zezhou Hao and Qing Chang
Biomedicines 2023, 11(5), 1479; https://doi.org/10.3390/biomedicines11051479 - 19 May 2023
Cited by 3 | Viewed by 1646
Abstract
Lung adenocarcinoma represents a significant global health challenge. Despite advances in diagnosis and treatment, the prognosis remains poor for many patients. In this study, we aimed to identify cuproptosis-related genes and to develop a deep neural network model to predict the prognosis of [...] Read more.
Lung adenocarcinoma represents a significant global health challenge. Despite advances in diagnosis and treatment, the prognosis remains poor for many patients. In this study, we aimed to identify cuproptosis-related genes and to develop a deep neural network model to predict the prognosis of lung adenocarcinoma. We screened differentially expressed genes from The Cancer Genome Atlas data through differential analysis of cuproptosis-related genes. We then used this information to establish a prognostic model using a deep neural network, which we validated using data from the Gene Expression Omnibus. Our deep neural network model incorporated nine cuproptosis-related genes and achieved an area under the curve of 0.732 in the training set and 0.646 in the validation set. The model effectively distinguished between distinct risk groups, as evidenced by significant differences in survival curves (p < 0.001), and demonstrated significant independence as a standalone prognostic predictor (p < 0.001). Functional analysis revealed differences in cellular pathways, the immune microenvironment, and tumor mutation burden between the risk groups. Furthermore, our model provided personalized survival probability predictions with a concordance index of 0.795 and identified the drug candidate BMS-754807 as a potentially sensitive treatment option for lung adenocarcinoma. In summary, we presented a deep neural network prognostic model for lung adenocarcinoma, based on nine cuproptosis-related genes, which offers independent prognostic capabilities. This model can be used for personalized predictions of patient survival and the identification of potential therapeutic agents for lung adenocarcinoma, which may ultimately improve patient outcomes. Full article
(This article belongs to the Special Issue Research and Application of Artificial Intelligence in Assisting Disease Diagnosis and Treatment)
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