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Chen W, Yi Z, Lim LJR, Lim RQR, Zhang A, Qian Z, Huang J, He J, Liu B. Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement. Front Bioeng Biotechnol 2024; 12:1420100. [PMID: 39104628 PMCID: PMC11298756 DOI: 10.3389/fbioe.2024.1420100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 06/27/2024] [Indexed: 08/07/2024] Open
Abstract
In recent decades, there has been ongoing development in the application of computer vision (CV) in the medical field. As conventional contact-based physiological measurement techniques often restrict a patient's mobility in the clinical environment, the ability to achieve continuous, comfortable and convenient monitoring is thus a topic of interest to researchers. One type of CV application is remote imaging photoplethysmography (rPPG), which can predict vital signs using a video or image. While contactless physiological measurement techniques have an excellent application prospect, the lack of uniformity or standardization of contactless vital monitoring methods limits their application in remote healthcare/telehealth settings. Several methods have been developed to improve this limitation and solve the heterogeneity of video signals caused by movement, lighting, and equipment. The fundamental algorithms include traditional algorithms with optimization and developing deep learning (DL) algorithms. This article aims to provide an in-depth review of current Artificial Intelligence (AI) methods using CV and DL in contactless physiological measurement and a comprehensive summary of the latest development of contactless measurement techniques for skin perfusion, respiratory rate, blood oxygen saturation, heart rate, heart rate variability, and blood pressure.
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Affiliation(s)
- Wei Chen
- Department of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
| | - Zhe Yi
- Department of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
| | - Lincoln Jian Rong Lim
- Department of Medical Imaging, Western Health, Footscray Hospital, Footscray, VIC, Australia
- Department of Surgery, The University of Melbourne, Melbourne, VIC, Australia
| | - Rebecca Qian Ru Lim
- Department of Hand & Reconstructive Microsurgery, Singapore General Hospital, Singapore, Singapore
| | - Aijie Zhang
- Department of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
| | - Zhen Qian
- Institute of Intelligent Diagnostics, Beijing United-Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Jiaxing Huang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jia He
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Bo Liu
- Department of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
- Beijing Research Institute of Traumatology and Orthopaedics, Beijing, China
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Kim J, Lee SM, Kim DE, Kim S, Chung MJ, Kim Z, Kim T, Lee KT. Development of an Automated Free Flap Monitoring System Based on Artificial Intelligence. JAMA Netw Open 2024; 7:e2424299. [PMID: 39058486 PMCID: PMC11282448 DOI: 10.1001/jamanetworkopen.2024.24299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 05/08/2024] [Indexed: 07/28/2024] Open
Abstract
Importance Meticulous postoperative flap monitoring is essential for preventing flap failure and achieving optimal results in free flap operations, for which physical examination has remained the criterion standard. Despite the high reliability of physical examination, the requirement of excessive use of clinician time has been considered a main drawback. Objective To develop an automated free flap monitoring system using artificial intelligence (AI), minimizing human involvement while maintaining efficiency. Design, Setting, and Participants In this prognostic study, the designed system involves a smartphone camera installed in a location with optimal flap visibility to capture photographs at regular intervals. The automated program identifies the flap area, checks for notable abnormalities in its appearance, and notifies medical staff if abnormalities are detected. Implementation requires 2 AI-based models: a segmentation model for automatic flap recognition in photographs and a grading model for evaluating the perfusion status of the identified flap. To develop this system, flap photographs captured for monitoring were collected from patients who underwent free flap-based reconstruction from March 1, 2020, to August 31, 2023. After the 2 models were developed, they were integrated to construct the system, which was applied in a clinical setting in November 2023. Exposure Conducting the developed automated AI-based flap monitoring system. Main Outcomes and Measures Accuracy of the developed models and feasibility of clinical application of the system. Results Photographs were obtained from 305 patients (median age, 62 years [range, 8-86 years]; 178 [58.4%] were male). Based on 2068 photographs, the FS-net program (a customized model) was developed for flap segmentation, demonstrating a mean (SD) Dice similarity coefficient of 0.970 (0.001) with 5-fold cross-validation. For the flap grading system, 11 112 photographs from the 305 patients were used, encompassing 10 115 photographs with normal features and 997 with abnormal features. Tested on 5506 photographs, the DenseNet121 model demonstrated the highest performance with an area under the receiver operating characteristic curve of 0.960 (95% CI, 0.951-0.969). The sensitivity for detecting venous insufficiency was 97.5% and for arterial insufficiency was 92.8%. When applied to 10 patients, the system successfully conducted 143 automated monitoring sessions without significant issues. Conclusions and Relevance The findings of this study suggest that a novel automated system may enable efficient flap monitoring with minimal use of clinician time. It may be anticipated to serve as an effective surveillance tool for postoperative free flap monitoring. Further studies are required to verify its reliability.
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Affiliation(s)
- Jisu Kim
- Department of Plastic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Sang Mee Lee
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, South Korea
- Medical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, South Korea
| | - Da Eun Kim
- Department of Plastic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Sungjin Kim
- Banobagi Plastic Surgery Clinic, Seoul, South Korea
| | - Myung Jin Chung
- Department of Data Convergence and Future Medicine, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Radiology and Medical AI Research Center, Samsung Medical Center, Seoul, South Korea
| | - Zero Kim
- Medical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, South Korea
- Department of Data Convergence and Future Medicine, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Taeyoung Kim
- Medical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, South Korea
| | - Kyeong-Tae Lee
- Department of Plastic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
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Liu L, Zhang Y, Xiao X, Xie R. The promising horizon of deep learning and artificial intelligence in flap monitoring. Int J Surg 2023; 109:4391-4392. [PMID: 37720927 PMCID: PMC10720854 DOI: 10.1097/js9.0000000000000748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 08/25/2023] [Indexed: 09/19/2023]
Affiliation(s)
| | - Ya Zhang
- Hengyang Medical School
- Department of Gland Surgery
| | - Xiangjun Xiao
- Hengyang Medical School
- Department of Hand and Foot Surgery, The Affiliated Nanhua Hospital, Hengyang Medical School, University of South China, Hengyang, People’s Republic of China
| | - Ruijie Xie
- Hengyang Medical School
- Department of Hand and Foot Surgery, The Affiliated Nanhua Hospital, Hengyang Medical School, University of South China, Hengyang, People’s Republic of China
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Li C, Liu W, Zhu Z, Wang X, Zhang Y. Quantization of extraoral free flap monitoring for venous congestion with deep learning integrated iOS applications on smartphones. Int J Surg 2023; 109:3679-3680. [PMID: 37462988 PMCID: PMC10651233 DOI: 10.1097/js9.0000000000000626] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 07/09/2023] [Indexed: 11/17/2023]
Affiliation(s)
- Chunyan Li
- Department of Clinical Laboratory
- Department of Laboratory Medicine, Beijing Jishuitan Hospital, Capital Medical University, Fourth Clinical College of Peking University, Beijing
| | - Wei Liu
- Department of Clinical Laboratory
- Department of Laboratory Medicine, Beijing Jishuitan Hospital, Capital Medical University, Fourth Clinical College of Peking University, Beijing
| | - Zhenglin Zhu
- Department of Orthopaedic Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing
| | - Xing Wang
- Department of Spinal Surgery, Shihezi General Hospital of the Eighth Division, Shihezi
| | - Yanbin Zhang
- Department of Spine Surgery, Beijing Jishuitan Hospital, Capital Medical University, Fourth Clinical College of Peking University, National Center for Orthopaedics, Beijing, People’s Republic of China
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Baecher H, Hoch CC, Knoedler S, Maheta BJ, Kauke-Navarro M, Safi AF, Alfertshofer M, Knoedler L. From bench to bedside - current clinical and translational challenges in fibula free flap reconstruction. Front Med (Lausanne) 2023; 10:1246690. [PMID: 37886365 PMCID: PMC10598714 DOI: 10.3389/fmed.2023.1246690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 09/29/2023] [Indexed: 10/28/2023] Open
Abstract
Fibula free flaps (FFF) represent a working horse for different reconstructive scenarios in facial surgery. While FFF were initially established for mandible reconstruction, advancements in planning for microsurgical techniques have paved the way toward a broader spectrum of indications, including maxillary defects. Essential factors to improve patient outcomes following FFF include minimal donor site morbidity, adequate bone length, and dual blood supply. Yet, persisting clinical and translational challenges hamper the effectiveness of FFF. In the preoperative phase, virtual surgical planning and artificial intelligence tools carry untapped potential, while the intraoperative role of individualized surgical templates and bioprinted prostheses remains to be summarized. Further, the integration of novel flap monitoring technologies into postoperative patient management has been subject to translational and clinical research efforts. Overall, there is a paucity of studies condensing the body of knowledge on emerging technologies and techniques in FFF surgery. Herein, we aim to review current challenges and solution possibilities in FFF. This line of research may serve as a pocket guide on cutting-edge developments and facilitate future targeted research in FFF.
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Affiliation(s)
- Helena Baecher
- Department of Plastic, Hand and Reconstructive Surgery, University Hospital Regensburg, Regensburg, Germany
| | - Cosima C. Hoch
- Medical Faculty, Friedrich Schiller University Jena, Jena, Germany
| | - Samuel Knoedler
- Division of Plastic Surgery, Department of Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT, United States
- Division of Plastic Surgery, Department of Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Department of Plastic Surgery and Hand Surgery, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Bhagvat J. Maheta
- College of Medicine, California Northstate University, Elk Grove, CA, United States
| | - Martin Kauke-Navarro
- Division of Plastic Surgery, Department of Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT, United States
| | - Ali-Farid Safi
- Craniologicum, Center for Cranio-Maxillo-Facial Surgery, Bern, Switzerland
- Faculty of Medicine, University of Bern, Bern, Switzerland
| | - Michael Alfertshofer
- Division of Hand, Plastic and Aesthetic Surgery, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Leonard Knoedler
- Department of Plastic, Hand and Reconstructive Surgery, University Hospital Regensburg, Regensburg, Germany
- Division of Plastic Surgery, Department of Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT, United States
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