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Su J, Liu Y, Zhang J, Han J, Song J. CDC-NET: a cell detection and confirmation network of bone marrow aspirate images for the aided diagnosis of AML. Med Biol Eng Comput 2024; 62:575-589. [PMID: 37953336 DOI: 10.1007/s11517-023-02955-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 10/20/2023] [Indexed: 11/14/2023]
Abstract
Standardized morphological evaluation in pathology is usually qualitative. Classifying and qualitatively analyzing the nucleated cells in the bone marrow aspirate images based on morphology is crucial for the diagnosis of acute myoid leukemia (AML), acute lymphoblastic leukemia (ALL), and Myelodysplastic syndrome (MDS), etc. However, it is time-consuming and difficult to accurately identify nucleated cells and calculate the percentage of the cells because of the complexity of bone marrow aspirate images. This paper proposed a deep learning analysis model of bone marrow aspirate images, termed Cell Detection and Confirmation Network (CDC-NET), for the aided diagnosis of AML by improving the accuracy of cell detection and recognition. Specifically, we take the nucleated cells in the bone marrow aspirate images as the detection objects to establish the model. Since some cells from different categories have similar morphology, classification error is inevitable. We design a confirmation network in which multiple trained classifiers work as pathologists to confirm the cell category by a voting method. To demonstrate the effectiveness of the proposed approach, experiments on clinical microscopic datasets are conducted. The Recall and Precision of CDC-NET are 78.54% and 91.74% respectively, and the missed rate of our method is lower than those of the other popular methods. The experimental results demonstrated that the proposed model has the potential for the pathological analysis of aspirate smears and the aided diagnosis of AML.
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Affiliation(s)
- Jie Su
- School of Information Science and Engineering, University of Jinan, Jinan, China.
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China.
| | - Yahui Liu
- School of Information Management, Beijing Information Science & Technology University, Beijing, China
| | - Jing Zhang
- School of Information Science and Engineering, University of Jinan, Jinan, China
| | - Jinjun Han
- School of Information Science and Engineering, University of Jinan, Jinan, China
| | - Jinming Song
- Department of Hematopathology and Lab Medicines, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
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Chen C, Chen Z, Luo W, Xu Y, Yang S, Yang G, Chen X, Chi X, Xie N, Zeng Z. Ethical perspective on AI hazards to humans: A review. Medicine (Baltimore) 2023; 102:e36163. [PMID: 38050218 PMCID: PMC10695628 DOI: 10.1097/md.0000000000036163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 10/26/2023] [Indexed: 12/06/2023] Open
Abstract
This article explores the potential ethical hazards of artificial intelligence (AI) on society from an ethical perspective. We introduce the development and application of AI, emphasizing its potential benefits and possible negative impacts. We particularly examine the application of AI in the medical field and related ethical and legal issues, and analyze potential hazards that may exist in other areas of application, such as autonomous driving, finance, and security. Finally, we offer recommendations to help policymakers, technology companies, and society as a whole address the potential hazards of AI. These recommendations include strengthening regulation and supervision of AI, increasing public understanding and awareness of AI, and actively exploring how to use the advantages of AI to achieve a more just, equal, and sustainable social development. Only by actively exploring the advantages of AI while avoiding its negative impacts can we better respond to future challenges.
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Affiliation(s)
- Changye Chen
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
| | - Ziyu Chen
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
| | - Wenyu Luo
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
- The School of Public Health, Guilin Medical University, Gui Lin, Guangxi, China
| | - Ying Xu
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
| | - Sixia Yang
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
| | - Guozhao Yang
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
| | - Xuhong Chen
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
| | - Xiaoxia Chi
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
| | - Ni Xie
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
| | - Zhuoying Zeng
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
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Daykan Y, O'Reilly BA. The role of artificial intelligence in the future of urogynecology. Int Urogynecol J 2023; 34:1663-1666. [PMID: 37486359 DOI: 10.1007/s00192-023-05612-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 07/08/2023] [Indexed: 07/25/2023]
Abstract
Artificial intelligence (AI) in medicine is a rapidly growing field aimed at using machine learning models to improve health outcomes and patient experiences. Many new platforms have become accessible and therefore it seems inevitable that we consider how to implement them in our day-to-day practice. Currently, the specialty of urogynecology faces new challenges as the population grows, life expectancy increases, and quality of life expectation is much improved. As AI has a lot of potential to promote the discipline of urogynecology, we aim to explore its abilities and possible use in the future. Challenges and risks are associated with using AI, and a responsible use of such resources is required.
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Affiliation(s)
- Yair Daykan
- Department of Urogynaecology, Cork University Maternity Hospital, Cork, Ireland.
- Department of Obstetrics and Gynecology, Meir Medical Center, Kfar Saba, Israel.
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Barry A O'Reilly
- Department of Urogynaecology, Cork University Maternity Hospital, Cork, Ireland
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