AI for Advanced Biomedical Applications

Artificial Intelligence (AI) is advancing biomedical science in many ways, from improving image-based diagnostics to engineering strategies for improving movement related to injury, birth defects, or neurological or cardiovascular disease, to predicting behavior and nerve responses to stimuli. BME Frontiers is assembling a special issue highlighting the latest applications of AI to biomedical science and invites you to submit your research for consideration.

The focus will be primarily on the application of AI to biomedical imaging, however, studies related to other biomedical applications of AI are welcome. If your research applies AI to any of these areas, we encourage you to submit: 

• Learning-based algorithms for biological or medical imaging, including disease diagnosis and prognosis, improving image clarity or resolution, and enabling cross-modality comparison
• Automated approaches, such as computer vision, to localizing and tracking moving targets
• Image-guided therapy, including planning for and execution of surgical procedures and during surgical interventions
• Image-omics and radiomics in disease diagnosis and therapy, including quantitative approaches to molecular diagnostic and therapeutic imaging
• Discovery-based applications of AI in biomedicine 
• Fusion of machine learning and domain knowledge, including feature extraction and computational modeling from biomedical images and annotation-efficient learning from biomedical imaging
• Biomedical image synthesis and editing
• Detection and segmentation from biomedical images

Professor Rama Chellappa, Johns Hopkins University
Professor S. Kevin Zhou, University of Science and Technology of China
Professor Jeremy Walston, Johns Hopkins School of Medicine

As articles within the special issue are published they will appear below.

A Deep Learning Approach for Detecting Colorectal Cancer via Raman Spectra

Zheng Cao, Xiang Pan, Hongyun Yu, Shiyuan Hua, Da Wang, Danny Z. Chen, Min Zhou, Jian Wu

BME Frontiers, vol. 2022, Article ID 9872028, 10 pages, 2022

Automatic Detection of Atrial Fibrillation from Single-Lead ECG Using Deep Learning of the Cardiac Cycle

Alina Dubatovka, Joachim M. Buhmann

BME Frontiers, vol. 2022, Article ID 9813062, 12 pages, 2022

Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma

Jifei Wang, Dasheng Wu, Meili Sun, Zhenpeng Peng, Yingyu Lin, Hongxin Lin, Jiazhao Chen, Tingyu Long, Zi-Ping Li, Chuanmiao Xie, Bingsheng Huang, Shi-Ting Feng

BME Frontiers, vol. 2022, Article ID 9793716, 12 pages, 2022

Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Autoencoders

Wei Xiong, Neil Yeung, Shubo Wang, Haofu Liao, Liyun Wang, Jiebo Luo

BME Frontiers, vol. 2022, Article ID 9763284, 10 pages, 2022

Connectivity-based Cortical Parcellation via Contrastive Learning on Spatial-Graph Convolution

Peiting You, Xiang Li, Fan Zhang, Quanzheng Li

BME Frontiers, vol. 2022, Article ID 9814824, 11 pages, 2022

Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer with Deep Learning

Hailing Liu, Yu Zhao, Fan Yang, Xiaoying Lou, Feng Wu, Hang Li, Xiaohan Xing, Tingying Peng, Bjoern Menze, Junzhou Huang, Shujun Zhang, Anjia Han, Jianhua Yao, Xinjuan Fan

BME Frontiers, vol. 2022, Article ID 9860179, 12 pages, 2022

Weakly- and Semisupervised Probabilistic Segmentation and Quantification of Reverberation Artifacts

Alex Ling Yu Hung, Edward Chen, John Galeotti

BME Frontiers, vol. 2022, Article ID 9837076, 15 pages, 2022

A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients

Lingyi Zhao, Muyinatu A. Lediju Bell

BME Frontiers, vol. 2022, Article ID 9780173, 17 pages, 2022

Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus

Angela Zhang, Amil Khan, Saisidharth Majeti, Judy Pham, Christopher Nguyen, Peter Tran, Vikram Iyer, Ashutosh Shelat, Jefferson Chen, B. S. Manjunath

BME Frontiers, vol. 2022, Article ID 9783128, 13 pages, 2022