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  1. Article ; Online: Bennett fracture combined with hamate fracture: carpometacarpal joint 'diagonal' fracture and dislocation: a case report.

    Wu, Wei-Bin / Du, Yun-Feng / Wang, Hong-Xing / Liang, Feng

    BMC musculoskeletal disorders

    2023  Volume 24, Issue 1, Page(s) 477

    Abstract: Background: Multiple carpometacarpal fractures and dislocations are rare. This case report describes a novel multiple carpometacarpal injury, namely, 'diagonal' carpometacarpal joint fracture and dislocation.: Case presentation: A 39-year-old male ... ...

    Abstract Background: Multiple carpometacarpal fractures and dislocations are rare. This case report describes a novel multiple carpometacarpal injury, namely, 'diagonal' carpometacarpal joint fracture and dislocation.
    Case presentation: A 39-year-old male general worker sustained a compression injury to his right hand in the dorsiflexion position. Radiography indicated a Bennett fracture, hamate fracture, and fracture at the base of the second metacarpal. Subsequent computed tomography and intraoperative examination confirmed an injury to the first to fourth carpometacarpal joint along a diagonal line. The normal anatomy of the patient's hand was successfully restored via open reduction combined with Kirschner wire and steel plate fixation.
    Conclusion: Our findings highlight the importance of taking the injury mechanism into account to avoid a missed diagnosis and to choose the best treatment approach. This is the first case of 'diagonal' carpometacarpal joint fracture and dislocation to be reported in the literature.
    MeSH term(s) Male ; Humans ; Adult ; Carpometacarpal Joints/diagnostic imaging ; Carpometacarpal Joints/surgery ; Fractures, Bone/complications ; Fractures, Bone/diagnostic imaging ; Joint Dislocations/complications ; Joint Dislocations/diagnostic imaging ; Wrist Injuries ; Hand Injuries/surgery ; Fractures, Multiple ; Multiple Trauma
    Language English
    Publishing date 2023-06-10
    Publishing country England
    Document type Case Reports ; Journal Article
    ZDB-ID 2041355-5
    ISSN 1471-2474 ; 1471-2474
    ISSN (online) 1471-2474
    ISSN 1471-2474
    DOI 10.1186/s12891-023-06588-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Screening and identifying hepatobiliary diseases through deep learning using ocular images: a prospective, multicentre study.

    Xiao, Wei / Huang, Xi / Wang, Jing Hui / Lin, Duo Ru / Zhu, Yi / Chen, Chuan / Yang, Ya Han / Xiao, Jun / Zhao, Lan Qin / Li, Ji-Peng Olivia / Cheung, Carol Yim-Lui / Mise, Yoshihiro / Guo, Zhi Yong / Du, Yun Feng / Chen, Bai Bing / Hu, Jing Xiong / Zhang, Kai / Lin, Xiao Shan / Wen, Wen /
    Liu, Yi Zhi / Chen, Wei Rong / Zhong, Yue Si / Lin, Hao Tian

    The Lancet. Digital health

    2021  Volume 3, Issue 2, Page(s) e88–e97

    Abstract: Background: Ocular changes are traditionally associated with only a few hepatobiliary diseases. These changes are non-specific and have a low detection rate, limiting their potential use as clinically independent diagnostic features. Therefore, we aimed ...

    Abstract Background: Ocular changes are traditionally associated with only a few hepatobiliary diseases. These changes are non-specific and have a low detection rate, limiting their potential use as clinically independent diagnostic features. Therefore, we aimed to engineer deep learning models to establish associations between ocular features and major hepatobiliary diseases and to advance automated screening and identification of hepatobiliary diseases from ocular images.
    Methods: We did a multicentre, prospective study to develop models using slit-lamp or retinal fundus images from participants in three hepatobiliary departments and two medical examination centres. Included participants were older than 18 years and had complete clinical information; participants diagnosed with acute hepatobiliary diseases were excluded. We trained seven slit-lamp models and seven fundus models (with or without hepatobiliary disease [screening model] or one specific disease type within six categories [identifying model]) using a development dataset, and we tested the models with an external test dataset. Additionally, we did a visual explanation and occlusion test. Model performances were evaluated using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and F1* score.
    Findings: Between Dec 16, 2018, and July 31, 2019, we collected data from 1252 participants (from the Department of Hepatobiliary Surgery of the Third Affiliated Hospital of Sun Yat-sen University, the Department of Infectious Diseases of the Affiliated Huadu Hospital of Southern Medical University, and the Nantian Medical Centre of Aikang Health Care [Guangzhou, China]) for the development dataset; between Aug 14, 2019, and Jan 31, 2020, we collected data from 537 participants (from the Department of Infectious Diseases of the Third Affiliated Hospital of Sun Yat-sen University and the Huanshidong Medical Centre of Aikang Health Care [Guangzhou, China]) for the test dataset. The AUROC for screening for hepatobiliary diseases of the slit-lamp model was 0·74 (95% CI 0·71-0·76), whereas that of the fundus model was 0·68 (0·65-0·71). For the identification of hepatobiliary diseases, the AUROCs were 0·93 (0·91-0·94; slit-lamp) and 0·84 (0·81-0·86; fundus) for liver cancer, 0·90 (0·88-0·91; slit-lamp) and 0·83 (0·81-0·86; fundus) for liver cirrhosis, and ranged 0·58-0·69 (0·55-0·71; slit-lamp) and 0·62-0·70 (0·58-0·73; fundus) for other hepatobiliary diseases, including chronic viral hepatitis, non-alcoholic fatty liver disease, cholelithiasis, and hepatic cyst. In addition to the conjunctiva and sclera, our deep learning model revealed that the structures of the iris and fundus also contributed to the classification.
    Interpretation: Our study established qualitative associations between ocular features and major hepatobiliary diseases, providing a non-invasive, convenient, and complementary method for hepatobiliary disease screening and identification, which could be applied as an opportunistic screening tool.
    Funding: Science and Technology Planning Projects of Guangdong Province; National Key R&D Program of China; Guangzhou Key Laboratory Project; National Natural Science Foundation of China.
    MeSH term(s) Adult ; Algorithms ; Area Under Curve ; China ; Computer Simulation ; Conjunctiva/diagnostic imaging ; Deep Learning ; Digestive System Diseases/complications ; Digestive System Diseases/diagnosis ; Eye/diagnostic imaging ; Fundus Oculi ; Humans ; Iris/diagnostic imaging ; Liver ; Mass Screening/methods ; Middle Aged ; Models, Biological ; Photography/methods ; Prospective Studies ; ROC Curve ; Sclera/diagnostic imaging ; Slit Lamp Microscopy/methods
    Language English
    Publishing date 2021-01-09
    Publishing country England
    Document type Journal Article ; Multicenter Study ; Research Support, Non-U.S. Gov't
    ISSN 2589-7500
    ISSN (online) 2589-7500
    DOI 10.1016/S2589-7500(20)30288-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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