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Condrea F, Rapaka S, Itu L, Sharma P, Sperl J, Ali AM, Leordeanu M. Anatomically aware dual-hop learning for pulmonary embolism detection in CT pulmonary angiograms. Comput Biol Med 2024; 174:108464. [PMID: 38613894 DOI: 10.1016/j.compbiomed.2024.108464] [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: 06/22/2023] [Revised: 04/01/2024] [Accepted: 04/08/2024] [Indexed: 04/15/2024]
Abstract
Pulmonary Embolisms (PE) represent a leading cause of cardiovascular death. While medical imaging, through computed tomographic pulmonary angiography (CTPA), represents the gold standard for PE diagnosis, it is still susceptible to misdiagnosis or significant diagnosis delays, which may be fatal for critical cases. Despite the recently demonstrated power of deep learning to bring a significant boost in performance in a wide range of medical imaging tasks, there are still very few published researches on automatic pulmonary embolism detection. Herein we introduce a deep learning based approach, which efficiently combines computer vision and deep neural networks for pulmonary embolism detection in CTPA. Our method brings novel contributions along three orthogonal axes: (1) automatic detection of anatomical structures; (2) anatomical aware pretraining, and (3) a dual-hop deep neural net for PE detection. We obtain state-of-the-art results on the publicly available multicenter large-scale RSNA dataset.
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Affiliation(s)
- Florin Condrea
- Institute of Mathematics of the Romanian Academy "Simion Stoilow, Bucharest, Romania; Advanta, Siemens, 15 Noiembrie Bvd, Brasov, 500097, Romania.
| | | | - Lucian Itu
- Advanta, Siemens, 15 Noiembrie Bvd, Brasov, 500097, Romania
| | | | | | - A Mohamed Ali
- Siemens Healthcare Private Limited, Mumbai, 400079, India
| | - Marius Leordeanu
- Institute of Mathematics of the Romanian Academy "Simion Stoilow, Bucharest, Romania; Advanta, Siemens, 15 Noiembrie Bvd, Brasov, 500097, Romania; Polytechnic University of Bucharest, Bucharest, Romania
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Wang Z, Feng N, Zhou Y, Cheng X, Zhou C, Ma A, Wang Q, Li Y, Chen Y. Mesophilic Argonaute-Mediated Polydisperse Droplet Biosensor for Amplification-Free, One-Pot, and Multiplexed Nucleic Acid Detection Using Deep Learning. Anal Chem 2024; 96:2068-2077. [PMID: 38259216 DOI: 10.1021/acs.analchem.3c04426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Detection of nucleic acids from a single multiplexed and amplification-free test is critical for ensuring food safety, clinical diagnostics, and environmental monitoring. In this study, we introduced a mesophilic Argonaute protein from Clostridium butyricum (CbAgo), which exhibits nucleic acid endonuclease activity, to achieve a programmable, amplification-free system (PASS) for rapid nucleic acid quantification at ambient temperatures in one pot. By using CbAgo-mediated binding with specific guide DNA (gDNA) and subsequent targeted cleavage of wild-type target DNAs complementary to gDNA, PASS can detect multiple foodborne pathogen DNA (<102 CFU/mL) simultaneously. The fluorescence signals were then transferred to polydisperse emulsions and analyzed by using deep learning. This simplifies the process and increases the suitability of polydisperse emulsions compared to traditional digital PCR, which requires homogeneous droplets for accurate detection. We believe that PASS has the potential to become a next-generation point-of-care digital nucleic acid detection method.
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Affiliation(s)
- Zhipan Wang
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| | - Niu Feng
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| | - Yanan Zhou
- State Key Laboratory of Agricultural Microbiology and College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| | - Xinrui Cheng
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| | - Cuiyun Zhou
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| | - Aimin Ma
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| | - Qinyu Wang
- Department of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430000, Hubei China
| | - Yingjun Li
- State Key Laboratory of Agricultural Microbiology and College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| | - Yiping Chen
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
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Kim K, Yang H, Lee J, Lee WG. Metaverse Wearables for Immersive Digital Healthcare: A Review. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2303234. [PMID: 37740417 PMCID: PMC10625124 DOI: 10.1002/advs.202303234] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/15/2023] [Indexed: 09/24/2023]
Abstract
The recent exponential growth of metaverse technology has been instrumental in reshaping a myriad of sectors, not least digital healthcare. This comprehensive review critically examines the landscape and future applications of metaverse wearables toward immersive digital healthcare. The key technologies and advancements that have spearheaded the metamorphosis of metaverse wearables are categorized, encapsulating all-encompassed extended reality, such as virtual reality, augmented reality, mixed reality, and other haptic feedback systems. Moreover, the fundamentals of their deployment in assistive healthcare (especially for rehabilitation), medical and nursing education, and remote patient management and treatment are investigated. The potential benefits of integrating metaverse wearables into healthcare paradigms are multifold, encompassing improved patient prognosis, enhanced accessibility to high-quality care, and high standards of practitioner instruction. Nevertheless, these technologies are not without their inherent challenges and untapped opportunities, which span privacy protection, data safeguarding, and innovation in artificial intelligence. In summary, future research trajectories and potential advancements to circumvent these hurdles are also discussed, further augmenting the incorporation of metaverse wearables within healthcare infrastructures in the post-pandemic era.
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Affiliation(s)
- Kisoo Kim
- Intelligent Optical Module Research CenterKorea Photonics Technology Institute (KOPTI)Gwangju61007Republic of Korea
| | - Hyosill Yang
- Department of NursingCollege of Nursing ScienceKyung Hee UniversitySeoul02447Republic of Korea
| | - Jihun Lee
- Department of Mechanical EngineeringCollege of EngineeringKyung Hee UniversityYongin17104Republic of Korea
| | - Won Gu Lee
- Department of Mechanical EngineeringCollege of EngineeringKyung Hee UniversityYongin17104Republic of Korea
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Sahiner B, Chen W, Samala RK, Petrick N. Data drift in medical machine learning: implications and potential remedies. Br J Radiol 2023; 96:20220878. [PMID: 36971405 PMCID: PMC10546450 DOI: 10.1259/bjr.20220878] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 02/16/2023] [Accepted: 02/20/2023] [Indexed: 03/29/2023] Open
Abstract
Data drift refers to differences between the data used in training a machine learning (ML) model and that applied to the model in real-world operation. Medical ML systems can be exposed to various forms of data drift, including differences between the data sampled for training and used in clinical operation, differences between medical practices or context of use between training and clinical use, and time-related changes in patient populations, disease patterns, and data acquisition, to name a few. In this article, we first review the terminology used in ML literature related to data drift, define distinct types of drift, and discuss in detail potential causes within the context of medical applications with an emphasis on medical imaging. We then review the recent literature regarding the effects of data drift on medical ML systems, which overwhelmingly show that data drift can be a major cause for performance deterioration. We then discuss methods for monitoring data drift and mitigating its effects with an emphasis on pre- and post-deployment techniques. Some of the potential methods for drift detection and issues around model retraining when drift is detected are included. Based on our review, we find that data drift is a major concern in medical ML deployment and that more research is needed so that ML models can identify drift early, incorporate effective mitigation strategies and resist performance decay.
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Affiliation(s)
- Berkman Sahiner
- Center for Devices and Radiological Health, U.S. Food and Drug Administration 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002
| | - Weijie Chen
- Center for Devices and Radiological Health, U.S. Food and Drug Administration 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002
| | - Ravi K. Samala
- Center for Devices and Radiological Health, U.S. Food and Drug Administration 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002
| | - Nicholas Petrick
- Center for Devices and Radiological Health, U.S. Food and Drug Administration 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002
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Xue H, Hu G, Hong N, Dunnick NR, Jin Z. How to keep artificial intelligence evolving in the medical imaging world? Challenges and opportunities. Sci Bull (Beijing) 2023; 68:648-652. [PMID: 36964087 DOI: 10.1016/j.scib.2023.03.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2023]
Affiliation(s)
- Huadan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Ge Hu
- Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing 100044, China.
| | - N Reed Dunnick
- Department of Radiology, University of Michigan, Ann Arbor 48109, USA.
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
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