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ISSN: 2957-5524
Email: MAI@zentimecorp.com
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Medical Artificial Intelligence (MAI) is an open-access, peer-reviewed online journal dedicated to the fast publication of research from a wide variety of interdisciplinary perspectives concerning the theory and application of artificial intelligence (AI) in medicine and health care. With the aim of promoting the development of medicine-related artificial intelligence, MAI provides an international forum for both the medical practitioners and AI technicians to share their cutting-edge research results in medicine-oriented AI in real word.


MAI is primarily devoted to publishing high-quality original research papers in all the fields related to AI and medical service which include but are not limited to robotic surgery system, disease diagnosis and prediction, image analysis, gene mutation prediction, medical statistics, human biology, omics technology, simulation and prediction of treatment effects and outcomes, and electronic medical records.


Please join us in this open-access endeavor by submitting your high-quality papers for publication in MAI.

Lastest Issue

A survey on the application of deep learning in knee joint cartilage ultrasound image segmentation

Jintao Duan1, Miao Zhou2, Yuxiang Wang1, Fangfang Chen1, Liangqing Lin3, Qinghua Wu3, Haipo Cui1

1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. 2Jiangsu Cancer Hospital, Nanjing 213164, Jiangsu Province, China. 3The First Hospital of Putian, Putian 351100, Fujian Province, China.

Address correspondence to: Haipo Cui, School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516 Jungong Road, Yangpu District, Shanghai 200093, China. E-mail: h_b_cui@163.com.

Received September 27, 2024; Accepted January 22, 2025; Published April 1, 2025

DOI: https://doi.org/10.61189/242224ubhmhy

Highlights

● A systematic review of deep learning (DL) approaches for femoral cartilage segmentation in knee joint ultrasound images.

● Evaluation of popular DL models (U-Net, U-Net++, Siam U-Net, Mask R-CNNs) using various metrics.

● Discussion on dataset scarcity, data preprocessing methods, and future directions for DL-based ultrasound segmentation.

Application of traditional methods and deep learning in breast ultrasound image segmentation

Fangfang Chen1, Miao Zhou2, Jintao Duan1, Yuxiang Wang1, Liangqing Lin4, Wenhui Guo3, Yongchu Hu5

1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. 2Jiangsu Cancer Hospital, Nanjing 213164, Jiangsu Province, China. 3School of Anesthesiology, Naval Medical University, Shanghai 200433, China. 4The First Hospital of Putian, Putian 351100, Fujian Province, China. 5The Department of Anesthesiology, Long March Hospital, Shanghai 200003, China.

Address correspondence to: Wenhui Guo, School of Anesthesiology, Naval Medical University, No.25 Zhongyuan Road, Shanghai 200093, China. E-mail: wendyguo17@outlook.com. Yongchu Hu, The De-partment of Anesthesiology, Long March Hospital, No.415 Fengyang Road, Shanghai 200003, China. E-mail address: Adsfoxcn@sina.com.cn.

DOI: https://doi.org/10.61189/341921wbvxvz

Received September 27, 2024; Accepted January 22, 2025; Published April 1, 2025

Highlights

● Comparison of Methods: This article compares traditional techniques, such as thresholding and edge detection, with deep learning-based methods for breast ultrasound segmentation.

● U-Net’s Effectiveness: U-Net is identified as the benchmark for medical image segmentation due to its efficiency and ability to preserve details.

● Advantages of Deep Learning: Deep learning models, like CNNs and FCNs, improve segmentation accuracy by learning directly from raw data, while also mitigating noise.

Application of U-Net and its variants in ultrasound image segmentation

Yuxiang Wang1, Miao Zhou2, Fangfang Chen1, Jintao Duan1, Liangqing Lin3, Qinghua Wu3, Wenhui Guo4, Haipo Cui1

1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. 2Jiangsu Cancer Hospital, Nanjing 213164, China. 3Anesthesiology, The First Hospital of Putian, Putian 351100, China. 4School of Anesthesiology, Second Military Medical University/Naval Medical University, Shanghai 200433, China.

Address correspondence to: Haipo Cui, School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516 Jungong Road, Yangpu District, Shanghai 200093, China. E-mail: h_b_cui@163.com.

Received September 27, 2024; Accepted January 22, 2025; Published April 1, 2025

DOI: https://doi.org/10.61189/861515qdddmg

Highlights

● This review provides a comprehensive overview of various U-Net architectures and their variants, including the original U-Net, U-Net++, Attention U-Net, and ResU-Net, along with a discussion on potential improvements to the

U- Net architecture.

● The differences between these network architectures are analyzed in terms of training complexity and computa-   tional requirements.

● The review delves into the application of U-Net and its variants in ultrasound imaging, discussing both the advan-   tages and limitations of each model in various ultrasound contexts. Relevant literature on the application of each network architecture in ultrasound is also summarized.
Read more
Most Read

Application of U-Net and its variants in ultrasound image segmentation

Yuxiang Wang1, Miao Zhou2, Fangfang Chen1, Jintao Duan1, Liangqing Lin3, Qinghua Wu3, Wenhui Guo4, Haipo Cui1

1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. 2Jiangsu Cancer Hospital, Nanjing 213164, China. 3Anesthesiology, The First Hospital of Putian, Putian 351100, China. 4School of Anesthesiology, Second Military Medical University/Naval Medical University, Shanghai 200433, China.

Address correspondence to: Haipo Cui, School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516 Jungong Road, Yangpu District, Shanghai 200093, China. E-mail: h_b_cui@163.com.

Received September 27, 2024; Accepted January 22, 2025; Published April 1, 2025

DOI: https://doi.org/10.61189/861515qdddmg

Highlights

● This review provides a comprehensive overview of various U-Net architectures and their variants, including the original U-Net, U-Net++, Attention U-Net, and ResU-Net, along with a discussion on potential improvements to the

U- Net architecture.

● The differences between these network architectures are analyzed in terms of training complexity and computa-   tional requirements.

● The review delves into the application of U-Net and its variants in ultrasound imaging, discussing both the advan-   tages and limitations of each model in various ultrasound contexts. Relevant literature on the application of each network architecture in ultrasound is also summarized.

Application of traditional methods and deep learning in breast ultrasound image segmentation

Fangfang Chen1, Miao Zhou2, Jintao Duan1, Yuxiang Wang1, Liangqing Lin4, Wenhui Guo3, Yongchu Hu5

1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. 2Jiangsu Cancer Hospital, Nanjing 213164, Jiangsu Province, China. 3School of Anesthesiology, Naval Medical University, Shanghai 200433, China. 4The First Hospital of Putian, Putian 351100, Fujian Province, China. 5The Department of Anesthesiology, Long March Hospital, Shanghai 200003, China.

Address correspondence to: Wenhui Guo, School of Anesthesiology, Naval Medical University, No.25 Zhongyuan Road, Shanghai 200093, China. E-mail: wendyguo17@outlook.com. Yongchu Hu, The De-partment of Anesthesiology, Long March Hospital, No.415 Fengyang Road, Shanghai 200003, China. E-mail address: Adsfoxcn@sina.com.cn.

DOI: https://doi.org/10.61189/341921wbvxvz

Received September 27, 2024; Accepted January 22, 2025; Published April 1, 2025

Highlights

● Comparison of Methods: This article compares traditional techniques, such as thresholding and edge detection, with deep learning-based methods for breast ultrasound segmentation.

● U-Net’s Effectiveness: U-Net is identified as the benchmark for medical image segmentation due to its efficiency and ability to preserve details.

● Advantages of Deep Learning: Deep learning models, like CNNs and FCNs, improve segmentation accuracy by learning directly from raw data, while also mitigating noise.

A survey on the application of deep learning in knee joint cartilage ultrasound image segmentation

Jintao Duan1, Miao Zhou2, Yuxiang Wang1, Fangfang Chen1, Liangqing Lin3, Qinghua Wu3, Haipo Cui1

1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. 2Jiangsu Cancer Hospital, Nanjing 213164, Jiangsu Province, China. 3The First Hospital of Putian, Putian 351100, Fujian Province, China.

Address correspondence to: Haipo Cui, School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516 Jungong Road, Yangpu District, Shanghai 200093, China. E-mail: h_b_cui@163.com.

Received September 27, 2024; Accepted January 22, 2025; Published April 1, 2025

DOI: https://doi.org/10.61189/242224ubhmhy

Highlights

● A systematic review of deep learning (DL) approaches for femoral cartilage segmentation in knee joint ultrasound images.

● Evaluation of popular DL models (U-Net, U-Net++, Siam U-Net, Mask R-CNNs) using various metrics.

● Discussion on dataset scarcity, data preprocessing methods, and future directions for DL-based ultrasound segmentation.
Read more