Article
Automation & Control Systems
Siqi Cai, Yizhi Liao, Lixuan Lai, Haiyu Zhou, Longhan Xie
Summary: This article introduces a computer-aided diagnosis method based on convolutional neural networks for generating corrective solutions for patients with pectus excavatum. By training a CNN model to predict the corrected sternum contours for patients, the effectiveness of the approach was validated through experiments.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Bruno Silva, Ines Pessanha, Jorge Correia-Pinto, Jaime C. Fonseca, Sandro Queiros
Summary: This paper proposes a fully automatic framework for quantifying PE severity from CT images by identifying anatomical keypoints and regularizing and extracting measurements, showing good agreement with the manual approach and improved reproducibility between indices extracted from different CTs of the same patient.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Review
Engineering, Biomedical
Haizhe Jin, Cheng Yu, Zibo Gong, Renjie Zheng, Yinan Zhao, Quanwei Fu
Summary: This study systematically analyzed and compared the performance of machine learning algorithms using the same dataset in the diagnosis of pulmonary nodules through a literature review.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Microbiology
Zhaotong Li, Fengliang Wu, Fengze Hong, Xiaoyan Gai, Wenli Cao, Zeru Zhang, Timin Yang, Jiu Wang, Song Gao, Chao Peng
Summary: The new deep learning method proposed effectively fuses four elaborate image features, showing optimal performance in accuracy, specificity, sensitivity, and area under curve when using VGG-11 and virtual data augmentation. There is an inverse relationship between model size and test accuracy.
FRONTIERS IN MICROBIOLOGY
(2022)
Article
Oncology
Huan Zheng, Zebin Xiao, Siwei Luo, Suqing Wu, Chuxin Huang, Tingting Hong, Yan He, Yanhui Guo, Guoqing Du
Summary: This study develops a computer aided diagnosis (CAD) system using deep learning to assist radiologists in diagnosing follicular thyroid carcinoma (FTC) on thyroid ultrasonography. The CAD system achieves better performance than radiologists and significantly improves their diagnosis of FTC. It provides a reliable reference for preoperative diagnosis of FTC and may assist in the development of a fast, accessible screening method for FTC.
FRONTIERS IN ONCOLOGY
(2022)
Article
Chemistry, Multidisciplinary
Thavavel Vaiyapuri, Liyakathunisa, Haya Alaskar, Ramasubramanian Parvathi, Venkatasubbu Pattabiraman, Abir Hussain
Summary: Lung cancer is a significant contributor to cancer-related mortality rate and early identification is crucial for improving survival. Computer-aided diagnosis models using computer vision and deep learning techniques have shown promise in precise detection and classification of lung cancer. This study presents a cat swarm optimization-based computer-aided diagnosis model for lung cancer classification, achieving improved performance through preprocessing, feature extraction, and parameter optimization.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Biomedical
Hitesh Tekchandani, Shrish Verma, Narendra D. Londhe, Rajiv Ratan Jain, Avani Tiwari
Summary: The study proposed a deep learning-based CADx system for the detection and diagnosis of cervical lymph nodes in head and neck cancer. Results showed that the system achieved high sensitivity and accuracy in both CLNs detection and diagnosis.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Computer Science, Information Systems
Mohammad Alamgeer, Hanan Abdullah Mengash, Radwa Marzouk, Mohamed K. Nour, Anwer Mustafa Hilal, Abdelwahed Motwakel, Abu Sarwar Zamani, Mohammed Rizwanullah
Summary: Early detection of lung cancer is crucial for improving patient survival rate. This article introduces a novel deep learning enabled CAD technique called DLCADLC-BCT for detection and classification of lung cancer using CT images. The simulation results demonstrate the superior performance of the DLCADLC-BCT technique compared to recent approaches.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Review
Engineering, Biomedical
Ziyi Jin, Tianyuan Gan, Peng Wang, Zuoming Fu, Chongan Zhang, Qinglai Yan, Xueyong Zheng, Xiao Liang, Xuesong Ye
Summary: This review summarizes the latest publications on the application of deep learning technology in gastroscopy. By providing on-site assistance, this technology can help endoscopists find and characterize lesions during real-time gastroscopy, thereby improving the quality of gastroscopy.
BIOMEDICAL ENGINEERING ONLINE
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Sohee Park, Sang Min Lee, Wooil Kim, Hyunho Park, Kyu-Hwan Jung, Kyung-Hyun Do, Joon Beom Seo
Summary: This study evaluated the impact of different CT section thicknesses on CAD performance in the detection of subsolid nodules. It was found that 1-mm section thickness CT achieved better detection results, especially for nonsolid nodules. Additionally, the use of a super-resolution algorithm improved CAD sensitivity at 3- and 5-mm section thickness CT.
Review
Multidisciplinary Sciences
Fabio Rosindo Daher de Barros, Caio Novais F. da Silva, Gabriel de Castro Michelassi, Helena Brentani, Fatima L. S. Nunes, Ariane Machado-Lima
Summary: Facial image analysis using image processing and machine learning techniques can help diagnose genetic syndromes and neurodevelopmental disorders. These systems offer faster and cost-effective alternatives for genotyping tests, especially for large-scale applications. However, ensuring the accuracy and reliability of computer-aided diagnosis systems remains a challenge.
Article
Computer Science, Artificial Intelligence
BingBing Zheng, Yu Zhu, Qin Shi, Dawei Yang, Yanmei Shao, Tao Xu
Summary: This paper proposes a mutex attention network based on deep learning for auxiliary diagnosis of COVID-19 on CT images, providing effective information for diagnosis.
APPLIED INTELLIGENCE
(2022)
Article
Engineering, Biomedical
Turimerla Pratap, Priyanka Kokil
Summary: Cataract is caused by denaturation of active protein cells in the eye lens, leading to cloudiness and affecting quality of life. Early diagnosis and treatment can reduce vision loss and delay cataract progression, while a computer-aided cataract diagnosis system can improve diagnostic accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Engineering, Electrical & Electronic
Lamiaa Abdel-Hamid
Summary: A novel two-branched deep convolutional (TWEEC) network is developed for computer-aided glaucoma diagnosis, achieving high accuracies by extracting anatomical information related to the optic disc and surrounding blood vessels. Experimental results show that the network outperforms other deep networks in spatial and wavelet inputs, with the advantage of reducing overall training time by considering specific wavelet subbands.
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
(2022)
Article
Oncology
Jing-Hang Ma, Shang-Feng You, Ji-Sen Xue, Xiao-Lin Li, Yi-Yao Chen, Yan Hu, Zhen Feng
Summary: Computer-aided diagnosis system plays an important role in cervical lesion diagnosis by using auto-segmented colposcopic images to extract features, augmenting minority data, and generating preliminary diagnosis results. The system improves sensitivity while maintaining acceptable specificity and accuracy.
FRONTIERS IN ONCOLOGY
(2022)
Article
Multidisciplinary Sciences
Shaopeng Zheng, Lintong Yao, Fasheng Li, Luyu Huang, Yunfang Yu, Zenan Lin, Hao Li, Jin Xia, Michael Lanuti, Haiyu Zhou
Summary: The study identified an up-regulation of the homologous recombination repair (HRR) pathway in LUAD patients, with RAD54L gene significantly differentially expressed in T1 stage LUAD. High expression of RAD54L was associated with worse overall survival in patients with T1 stage LUAD.
Article
Immunology
Duo Chen, Luyu Huang, Haiyu Zhou, Yuhui Zhang
Summary: Oncolytic viruses combined with interleukin 10 (IL-10) can enhance antitumor efficacy significantly, especially depending on CD8(+) T cells. The combination therapy induced long-term tumor-specific immune memory in a mouse model, rejecting rechallenge by specific tumor cell lines.
FRONTIERS IN IMMUNOLOGY
(2021)
Article
Oncology
Hongyuan Zhu, Daipeng Xie, Yunfang Yu, Lintong Yao, Bin Xu, Luyu Huang, Shaowei Wu, Fasheng Li, Yating Zheng, Xinyi Liu, Wenzhuan Xie, Mengli Huang, Hao Li, Shaopeng Zheng, Dongkun Zhang, Guibin Qiao, Lawrence W. C. Chan, Haiyu Zhou
Summary: The study revealed that patients with KEAP1/NFE2L2 mutations have a worse prognosis than wild-type patients, both on immunotherapy and chemotherapy. In addition, in patients with KEAP1/NFE2L2 mutations, immunotherapy did not significantly improve prognosis compared to chemotherapy.
FRONTIERS IN ONCOLOGY
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Luyu Huang, Weihuan Lin, Daipeng Xie, Yunfang Yu, Hanbo Cao, Guoqing Liao, Shaowei Wu, Lintong Yao, Zhaoyu Wang, Mei Wang, Siyun Wang, Guangyi Wang, Dongkun Zhang, Su Yao, Zifan He, William Chi-Shing Cho, Duo Chen, Zhengjie Zhang, Wanshan Li, Guibin Qiao, Lawrence Wing-Chi Chan, Haiyu Zhou
Summary: This study demonstrated the potential of a nomogram constructed by identified clinical-radiological signatures and combined radiomic signatures to precisely predict pathology invasiveness. The radiomic nomogram outperformed any clinical or radiomic signature in terms of clinical predictive abilities.
EUROPEAN RADIOLOGY
(2022)
Article
Oncology
Guoqing Liao, Luyu Huang, Shaowei Wu, Peirong Zhang, Daipeng Xie, Lintong Yao, Zhengjie Zhang, Su Yao, Lyu Shanshan, Siyun Wang, Guangyi Wang, Lawrence Wing-Chi Chan, Haiyu Zhou
Summary: This study aims to develop and evaluate CT-based radiomic features to predict STAS status in clinical stage I LUAD. The results show that peritumoral radiomic characteristics are significantly related to STAS status and the radiomic signature combined with clinical signature has better performance in predicting STAS status.
Article
Oncology
Qiaxuan Li, Lintong Yao, Zenan Lin, Fasheng Li, Daipeng Xie, Congsen Li, Weijie Zhan, Weihuan Lin, Luyu Huang, Shaowei Wu, Haiyu Zhou
Summary: This study constructed a prognostic model based on 15 immune-related lncRNAs for NSCLC, showing better survival outcomes in the low-risk group. The combination of tumor staging systems with immune-related lncRNA signatures demonstrated higher prognostic efficacy. The low-risk group had higher immune cell infiltration, immune scores, and response rates to immunotherapy, with critical pathways enriched in immune response and cytoskeleton.
FRONTIERS IN ONCOLOGY
(2021)
Article
Medicine, General & Internal
Hai-Yu Zhou, Shao-Peng Zheng, An-Lin Li, Quan-Long Gao, Qi-Yun Ou, Yong-Jian Chen, Shao-Tao Wu, Da-Gui Lin, Sheng-Bo Liu, Lu-Yu Huang, Fa-Sheng Li, Hong-Yuan Zhu, Gui-Bin Qiao, Michael Lanuti, He-Rui Yao, Yun-Fang Yu
Article
Materials Science, Biomaterials
Fanjun Zeng, Bin Xu, Hongyuan Zhu, Shaowei Wu, Guoqing Liao, Daipeng Xie, Luyu Huang, Guibin Qiao, Xianzhu Yang, Haiyu Zhou
BIOMATERIALS SCIENCE
(2020)