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Rouzrokh P, Mickley JP, Khosravi B, Faghani S, Moassefi M, Schulz WR, Erickson BJ, Taunton MJ, Wyles CC. THA-AID: Deep Learning Tool for Total Hip Arthroplasty Automatic Implant Detection With Uncertainty and Outlier Quantification. J Arthroplasty 2024; 39:966-973.e17. [PMID: 37770007 DOI: 10.1016/j.arth.2023.09.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/13/2023] [Accepted: 09/16/2023] [Indexed: 10/03/2023] Open
Abstract
BACKGROUND Revision total hip arthroplasty (THA) requires preoperatively identifying in situ implants, a time-consuming and sometimes unachievable task. Although deep learning (DL) tools have been attempted to automate this process, existing approaches are limited by classifying few femoral and zero acetabular components, only classify on anterior-posterior (AP) radiographs, and do not report prediction uncertainty or flag outlier data. METHODS This study introduces Total Hip Arhtroplasty Automated Implant Detector (THA-AID), a DL tool trained on 241,419 radiographs that identifies common designs of 20 femoral and 8 acetabular components from AP, lateral, or oblique views and reports prediction uncertainty using conformal prediction and outlier detection using a custom framework. We evaluated THA-AID using internal, external, and out-of-domain test sets and compared its performance with human experts. RESULTS THA-AID achieved internal test set accuracies of 98.9% for both femoral and acetabular components with no significant differences based on radiographic view. The femoral classifier also achieved 97.0% accuracy on the external test set. Adding conformal prediction increased true label prediction by 0.1% for acetabular and 0.7 to 0.9% for femoral components. More than 99% of out-of-domain and >89% of in-domain outlier data were correctly identified by THA-AID. CONCLUSIONS The THA-AID is an automated tool for implant identification from radiographs with exceptional performance on internal and external test sets and no decrement in performance based on radiographic view. Importantly, this is the first study in orthopedics to our knowledge including uncertainty quantification and outlier detection of a DL model.
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Affiliation(s)
| | - John P Mickley
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota
| | | | | | - Mana Moassefi
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - William R Schulz
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota
| | | | | | - Cody C Wyles
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota
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Tiwari A, Yadav AK, Akshay K, Bagaria V. Evaluation of machine learning models to identify hip arthroplasty implants using transfer learning algorithms. J Clin Orthop Trauma 2023; 47:102312. [PMID: 38196501 PMCID: PMC10772396 DOI: 10.1016/j.jcot.2023.102312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 12/06/2023] [Indexed: 01/11/2024] Open
Affiliation(s)
- Anjali Tiwari
- Department of Orthopedic Surgery, Sir H N Reliance Foundation Hospital, Girgaum, Mumbai, Maharashtra, India
| | - Amit Kumar Yadav
- International Training Fellow, Department of Trauma & Orthopedic Surgery, Wrightington Hospital, Wigan, UK
| | - K.S. Akshay
- Grant Government Medical College and Sir J J Group of Hospitals, India
| | - Vaibhav Bagaria
- Department of Orthopedic Surgery, Sir H N Reliance Foundation Hospital, Girgaum, Mumbai, Maharashtra, India
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Shah AK, Lavu MS, Hecht CJ, Burkhart RJ, Kamath AF. Understanding the use of artificial intelligence for implant analysis in total joint arthroplasty: a systematic review. ARTHROPLASTY 2023; 5:54. [PMID: 37919812 PMCID: PMC10623774 DOI: 10.1186/s42836-023-00209-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 09/01/2023] [Indexed: 11/04/2023] Open
Abstract
INTRODUCTION In recent years, there has been a significant increase in the development of artificial intelligence (AI) algorithms aimed at reviewing radiographs after total joint arthroplasty (TJA). This disruptive technology is particularly promising in the context of preoperative planning for revision TJA. Yet, the efficacy of AI algorithms regarding TJA implant analysis has not been examined comprehensively. METHODS PubMed, EBSCO, and Google Scholar electronic databases were utilized to identify all studies evaluating AI algorithms related to TJA implant analysis between 1 January 2000, and 27 February 2023 (PROSPERO study protocol registration: CRD42023403497). The mean methodological index for non-randomized studies score was 20.4 ± 0.6. We reported the accuracy, sensitivity, specificity, positive predictive value, and area under the curve (AUC) for the performance of each outcome measure. RESULTS Our initial search yielded 374 articles, and a total of 20 studies with three main use cases were included. Sixteen studies analyzed implant identification, two addressed implant failure, and two addressed implant measurements. Each use case had a median AUC and accuracy above 0.90 and 90%, respectively, indicative of a well-performing AI algorithm. Most studies failed to include explainability methods and conduct external validity testing. CONCLUSION These findings highlight the promising role of AI in recognizing implants in TJA. Preliminary studies have shown strong performance in implant identification, implant failure, and accurately measuring implant dimensions. Future research should follow a standardized guideline to develop and train models and place a strong emphasis on transparency and clarity in reporting results. LEVEL OF EVIDENCE Level III.
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Affiliation(s)
- Aakash K Shah
- Department of Orthopaedic Surgery, Cleveland Clinic Foundation, Cleveland, OH, 44195, USA
| | - Monish S Lavu
- Department of Orthopaedic Surgery, Cleveland Clinic Foundation, Cleveland, OH, 44195, USA
| | - Christian J Hecht
- Department of Orthopaedic Surgery, Cleveland Clinic Foundation, Cleveland, OH, 44195, USA
| | - Robert J Burkhart
- Department of Orthopaedic Surgery, University Hospitals, Cleveland, OH, 44106, USA
| | - Atul F Kamath
- Department of Orthopaedic Surgery, Cleveland Clinic Foundation, Cleveland, OH, 44195, USA.
- Center for Hip Preservation, Orthopaedic and Rheumatologic Institute, Cleveland Clinic Foundation, 9500 Euclid Avenue, Mail Code A41, Cleveland, OH, 44195, USA.
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Padash S, Mickley JP, Vera-Garcia DV, Nugen F, Khosravi B, Erickson BJ, Wyles CC, Taunton MJ. An Overview of Machine Learning in Orthopedic Surgery: An Educational Paper. J Arthroplasty 2023; 38:1938-1942. [PMID: 37598786 PMCID: PMC10601337 DOI: 10.1016/j.arth.2023.08.043] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 08/10/2023] [Accepted: 08/11/2023] [Indexed: 08/22/2023] Open
Abstract
The growth of artificial intelligence combined with the collection and storage of large amounts of data in the electronic medical record collection has created an opportunity for orthopedic research and translation into the clinical environment. Machine learning (ML) is a type of artificial intelligence tool well suited for processing the large amount of available data. Specific areas of ML frequently used by orthopedic surgeons performing total joint arthroplasty include tabular data analysis (spreadsheets), medical imaging processing, and natural language processing (extracting concepts from text). Previous studies have discussed models able to identify fractures in radiographs, identify implant type in radiographs, and determine the stage of osteoarthritis based on walking analysis. Despite the growing popularity of ML, there are limitations including its reliance on "good" data, potential for overfitting, long life cycle for creation, and ability to only perform one narrow task. This educational article will further discuss a general overview of ML, discussing these challenges and including examples of successfully published models.
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Affiliation(s)
- Sirwa Padash
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Mayo Clinic, Rochester, MN
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN
| | - John P. Mickley
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Mayo Clinic, Rochester, MN
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN
| | - Diana Victoria Vera-Garcia
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Mayo Clinic, Rochester, MN
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN
| | - Fred Nugen
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Mayo Clinic, Rochester, MN
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN
| | - Bardia Khosravi
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Mayo Clinic, Rochester, MN
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN
| | - Bradley J. Erickson
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN
| | - Cody C. Wyles
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Mayo Clinic, Rochester, MN
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN
| | - Michael J. Taunton
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Mayo Clinic, Rochester, MN
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN
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Karnuta JM, Murphy MP, Luu BC, Ryan MJ, Haeberle HS, Brown NM, Iorio R, Chen AF, Ramkumar PN. Artificial Intelligence for Automated Implant Identification in Total Hip Arthroplasty: A Multicenter External Validation Study Exceeding Two Million Plain Radiographs. J Arthroplasty 2023; 38:1998-2003.e1. [PMID: 35271974 DOI: 10.1016/j.arth.2022.03.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 02/23/2022] [Accepted: 03/01/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND The surgical management of complications after total hip arthroplasty (THA) necessitates accurate identification of the femoral implant manufacturer and model. Automated image processing using deep learning has been previously developed and internally validated; however, external validation is necessary prior to responsible application of artificial intelligence (AI)-based technologies. METHODS We trained, validated, and externally tested a deep learning system to classify femoral-sided THA implants as one of the 8 models from 2 manufacturers derived from 2,954 original, deidentified, retrospectively collected anteroposterior plain radiographs across 3 academic referral centers and 13 surgeons. From these radiographs, 2,117 were used for training, 249 for validation, and 588 for external testing. Augmentation was applied to the training set (n = 2,117,000) to increase model robustness. Performance was evaluated by area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Implant identification processing speed was calculated. RESULTS The training and testing sets were drawn from statistically different populations of implants (P < .001). After 1,000 training epochs by the deep learning system, the system discriminated 8 implant models with a mean area under the receiver operating characteristic curve of 0.991, accuracy of 97.9%, sensitivity of 88.6%, and specificity of 98.9% in the external testing dataset of 588 anteroposterior radiographs. The software classified implants at a mean speed of 0.02 seconds per image. CONCLUSION An AI-based software demonstrated excellent internal and external validation. Although continued surveillance is necessary with implant library expansion, this software represents responsible and meaningful clinical application of AI with immediate potential to globally scale and assist in preoperative planning prior to revision THA.
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Affiliation(s)
- Jaret M Karnuta
- Orthopaedic Machine Learning Laboratory, Orthopaedic Intelligence LLC, Cleveland Heights, OH; Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, PA
| | - Michael P Murphy
- Department of Orthopaedic Surgery & Rehabilitation, Loyola University Medical Center, Chicago, IL
| | - Bryan C Luu
- Orthopaedic Machine Learning Laboratory, Orthopaedic Intelligence LLC, Cleveland Heights, OH; Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, TX
| | - Michael J Ryan
- Orthopaedic Machine Learning Laboratory, Orthopaedic Intelligence LLC, Cleveland Heights, OH
| | - Heather S Haeberle
- Orthopaedic Machine Learning Laboratory, Orthopaedic Intelligence LLC, Cleveland Heights, OH; Sports Medicine Institute, Hospital for Special Surgery, New York, NY
| | - Nicholas M Brown
- Department of Orthopaedic Surgery & Rehabilitation, Loyola University Medical Center, Chicago, IL
| | - Richard Iorio
- Department of Orthopaedic Surgery, Brigham & Women's Hospital, Boston, MA
| | - Antonia F Chen
- Department of Orthopaedic Surgery, Brigham & Women's Hospital, Boston, MA
| | - Prem N Ramkumar
- Orthopaedic Machine Learning Laboratory, Orthopaedic Intelligence LLC, Cleveland Heights, OH; Sports Medicine Institute, Hospital for Special Surgery, New York, NY; Department of Orthopaedic Surgery, Brigham & Women's Hospital, Boston, MA
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Kunze KN, Jang SJ, Li TY, Pareek A, Finocchiaro A, Fu MC, Taylor SA, Dines JS, Dines DM, Warren RF, Gulotta LV. Artificial intelligence for automated identification of total shoulder arthroplasty implants. J Shoulder Elbow Surg 2023; 32:2115-2122. [PMID: 37172888 DOI: 10.1016/j.jse.2023.03.028] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/03/2023] [Accepted: 03/22/2023] [Indexed: 05/15/2023]
Abstract
BACKGROUND Accurate and rapid identification of implant manufacturer and model is critical in the evaluation and management of patients requiring revision total shoulder arthroplasty (TSA). Failure to correctly identify implant designs in these circumstances may lead to delay in care, unexpected intraoperative challenges, increased morbidity, and excess health care costs. Deep learning (DL) permits automated image processing and holds the potential to mitigate such challenges while improving the value of care rendered. The purpose of this study was to develop an automated DL algorithm to identify shoulder arthroplasty implants from plain radiographs. METHODS A total of 3060 postoperative images from patients who underwent TSA between 2011 and 2021 performed by 26 fellowship-trained surgeons at 2 independent tertiary academic hospitals in the Pacific Northwest and Mid-Atlantic Northeast were included. A DL algorithm was trained using transfer learning and data augmentation to classify 22 different reverse TSA and anatomic TSA prostheses from 8 implant manufacturers. Images were split into training and testing cohorts (2448 training and 612 testing images). Optimized model performance was assessed using standardized metrics including the multiclass area under the receiver operating characteristic curve (AUROC) and compared with a reference standard of implant data from operative reports. RESULTS The algorithm classified implants at a mean speed of 0.079 seconds (±0.002 seconds) per image. The optimized model discriminated between 8 manufacturers (22 unique implants) with AUROCs of 0.994-1.000, accuracy of 97.1%, and sensitivities between 0.80 and 1.00 on the independent testing set. In the subset of single-institution implant predictions, a DL model identified 6 specific implants with AUROCs of 0.999-1.000, accuracy of 99.4%, and sensitivity >0.97 for all implants. Saliency maps revealed key differentiating features across implant manufacturers and designs recognized by the algorithm for classification. CONCLUSION A DL model demonstrated excellent accuracy in identifying 22 unique TSA implants from 8 manufacturers. This algorithm may provide a clinically meaningful adjunct in assisting with preoperative planning for the failed TSA and allows for scalable expansion with additional radiographic data and validation efforts.
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Affiliation(s)
- Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA; Sports Medicine and Shoulder Institute, Hospital for Special Surgery, New York, NY, USA.
| | | | - Tim Y Li
- Weill Cornell College of Medicine, New York, NY, USA
| | - Ayoosh Pareek
- Sports Medicine and Shoulder Institute, Hospital for Special Surgery, New York, NY, USA
| | - Anthony Finocchiaro
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA; Sports Medicine and Shoulder Institute, Hospital for Special Surgery, New York, NY, USA
| | - Michael C Fu
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA; Sports Medicine and Shoulder Institute, Hospital for Special Surgery, New York, NY, USA
| | - Samuel A Taylor
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA; Sports Medicine and Shoulder Institute, Hospital for Special Surgery, New York, NY, USA
| | - Joshua S Dines
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA; Sports Medicine and Shoulder Institute, Hospital for Special Surgery, New York, NY, USA
| | - David M Dines
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA; Sports Medicine and Shoulder Institute, Hospital for Special Surgery, New York, NY, USA
| | - Russell F Warren
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA; Sports Medicine and Shoulder Institute, Hospital for Special Surgery, New York, NY, USA
| | - Lawrence V Gulotta
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA; Sports Medicine and Shoulder Institute, Hospital for Special Surgery, New York, NY, USA
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Chen Y, Sun Q, Li Z, Zhong Y, Zeng J, Nie T. Development and validation of a deep learning model using convolutional neural networks to identify femoral internal fixation device in radiographs. Skeletal Radiol 2023:10.1007/s00256-023-04324-5. [PMID: 36964792 DOI: 10.1007/s00256-023-04324-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/13/2023] [Accepted: 03/13/2023] [Indexed: 03/26/2023]
Abstract
OBJECTIVE The purpose of this study is to develop and validate a deep convolutional neural network (DCNN) model to automatically identify the manufacturer and model of hip internal fixation devices from anteroposterior (AP) radiographs. MATERIALS AND METHODS In this retrospective study, 1721 hip AP radiographs, including six internal fixation devices from 1012 patients, were collected from an orthopedic center between June 2014 and June 2022 to establish a classification network. The images were divided into training set (1106 images), validation set (272 images), and test set (343 images). The model efficacy is evaluated by using the data on the test set. The overall TOP-1 accuracy, and the precision, sensitivity, specificity, and F1 score of each model are calculated, and receiver operating characteristic (ROC) curves are plotted to evaluate the model performance. Gradient-weighted class activation mapping (Grad-CAM) images are used to determine the image features that are most important for DCNN decisions. RESULTS A total of 1378 (80%) images were used for model development, and model efficacy was validated on a test set with 343 (20%) images. The overall TOP-1 accuracy was 98.5%. The area under the receiver operating characteristic curve (AUC) values for each internal fixation model were 1.000, 1.000, 0.980, 1.000, 0.999, and 1.000, respectively. Gradient-weighted class activation mapping showed the unique design of the internal fixation device. CONCLUSION We developed a deep convolutional neural network model that can identify the manufacturer and model of hip internal fixation devices from the hip AP radiographs.
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Affiliation(s)
- Yanzhen Chen
- Deparment of Orthopedics, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Qian Sun
- Software Engineering Institute, East China Normal University, Shanghai, China
| | - Zhipeng Li
- Deparment of Orthopedics, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yuanwu Zhong
- Deparment of Orthopedics, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Junfeng Zeng
- Deparment of Orthopedics, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Tao Nie
- Deparment of Orthopedics, The First Affiliated Hospital of Nanchang University, Nanchang, China.
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Geng EA, Cho BH, Valliani AA, Arvind V, Patel AV, Cho SK, Kim JS, Cagle PJ. Development of a machine learning algorithm to identify total and reverse shoulder arthroplasty implants from X-ray images. J Orthop 2023; 35:74-78. [PMID: 36411845 PMCID: PMC9674869 DOI: 10.1016/j.jor.2022.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/16/2022] [Accepted: 11/07/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Demand for total shoulder arthroplasty (TSA) has risen significantly and is projected to continue growing. From 2012 to 2017, the incidence of reverse total shoulder arthroplasty (rTSA) rose from 7.3 cases per 100,000 to 19.3 per 100,000. Anatomical TSA saw a growth from 9.5 cases per 100,000 to 12.5 per 100,000. Failure to identify implants in a timely manner can increase operative time, cost and risk of complications. Several machine learning models have been developed to perform medical image analysis. However, they have not been widely applied in shoulder surgery. The authors developed a machine learning model to identify shoulder implant manufacturers and type from anterior-posterior X-ray images. Methods The model deployed was a convolutional neural network (CNN), which has been widely used in computer vision tasks. 696 radiographs were obtained from a single institution. 70% were used to train the model, while evaluation was done on 30%. Results On the evaluation set, the model performed with an overall accuracy of 93.9% with positive predictive value, sensitivity and F-1 scores of 94% across 10 different implant types (4 reverse, 6 anatomical). Average identification time was 0.110 s per implant. Conclusion This proof of concept study demonstrates that machine learning can assist with preoperative planning and improve cost-efficiency in shoulder surgery.
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Affiliation(s)
- Eric A. Geng
- Department of Orthopaedic Surgery, Mount Sinai Health System, New York, NY, 10029, USA
| | - Brian H. Cho
- Department of Orthopaedic Surgery, Mount Sinai Health System, New York, NY, 10029, USA
| | - Aly A. Valliani
- Department of Orthopaedic Surgery, Mount Sinai Health System, New York, NY, 10029, USA
| | - Varun Arvind
- Department of Orthopaedic Surgery, Mount Sinai Health System, New York, NY, 10029, USA
| | - Akshar V. Patel
- Department of Orthopaedic Surgery, Mount Sinai Health System, New York, NY, 10029, USA
| | - Samuel K. Cho
- Department of Orthopaedic Surgery, Mount Sinai Health System, New York, NY, 10029, USA
| | - Jun S. Kim
- Department of Orthopaedic Surgery, Mount Sinai Health System, New York, NY, 10029, USA
| | - Paul J. Cagle
- Department of Orthopaedic Surgery, Mount Sinai Health System, New York, NY, 10029, USA
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Morris MX, Rajesh A, Asaad M, Hassan A, Saadoun R, Butler CE. Deep Learning Applications in Surgery: Current Uses and Future Directions. Am Surg 2023; 89:36-42. [PMID: 35567312 DOI: 10.1177/00031348221101490] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Deep learning (DL) is a subset of machine learning that is rapidly gaining traction in surgical fields. Its tremendous capacity for powerful data-driven problem-solving has generated computational breakthroughs in many realms, with the fields of medicine and surgery becoming increasingly prominent avenues. Through its multi-layer architecture of interconnected neural networks, DL enables feature extraction and pattern recognition of highly complex and large-volume data. Across various surgical specialties, DL is being applied to optimize both preoperative planning and intraoperative performance in new and innovative ways. Surgeons are now able to integrate deep learning tools into their practice to improve patient safety and outcomes. Through this review, we explore the applications of deep learning in surgery and related subspecialties with an aim to shed light on the practical utilization of this technology in the present and near future.
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Affiliation(s)
- Miranda X Morris
- 12277Duke University School of Medicine, Durham, NC, USA.,101571Duke Pratt School of Engineering, Durham, NC, USA
| | - Aashish Rajesh
- Department of Surgery, 14742University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Malke Asaad
- Department of Plastic Surgery, 6595University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Abbas Hassan
- Department of Plastic Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rakan Saadoun
- Department of Plastic Surgery, 6595University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Charles E Butler
- Department of Plastic Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Wilson NA, Tcheng JE, Graham J, Drozda JP. Advancing Patient Safety Surrounding Medical Devices: Barriers, Strategies, and Next Steps in Health System Implementation of Unique Device Identifiers. MEDICAL DEVICES-EVIDENCE AND RESEARCH 2022; 15:177-186. [PMID: 35761948 PMCID: PMC9233486 DOI: 10.2147/mder.s364539] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 06/09/2022] [Indexed: 12/21/2022] Open
Abstract
Background The requirement for medical device manufacturers to label their devices with a unique device identifier (UDI) was formalized by the 2013 US Food and Drug Administration Unique Device Identification System Rule. However, parallel regulatory requirement for US health systems to use UDIs, particularly the electronic documentation of UDIs during patient care is lacking. Despite the lack of regulation, some health systems have implemented and are using UDIs. To assess the current state, we studied representative health system UDI implementation experiences, including barriers and the strategies to overcome them, and identified next steps to advance UDI adoption. Methods Semi-structured interviews were performed with health system personnel involved in UDI implementation in their cardiac catheterization labs or operating rooms. Interviews were transcribed and analyzed using the framework methodology of Ritchie and Spencer. An expert panel evaluated findings and informed barriers, strategies, and next steps. Results Twenty-four interviews at ten health systems were performed. Identified barriers were internal (lack of organizational support, information technology gaps, clinical resistance) and external (information technology vendor resistance, limitations in manufacturer support, gaps in reference data, lack of an overall UDI system). Identified strategies included relationship building, education, engagement, and communication. Next steps to advance UDI adoption focus on education, research, support, and policy. Conclusions and Implications Delineation of UDI implementation barriers and strategies provides guidance and support for health systems to adopt the UDI standard and electronically document UDIs during clinical care. Next steps illuminate critical areas for attention to advance UDI adoption and achieve a comprehensive UDI system in health care to strengthen patient care and safety.
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Affiliation(s)
- Natalia A Wilson
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
| | - James E Tcheng
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Jove Graham
- Center for Pharmacy Innovation and Outcomes, Geisinger, Danville, PA, USA
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Bajow N, Alkhalil S, Maghraby N, Alesa S, Najjar AA, Aloraifi S. Assessment of the effectiveness of a course in major chemical incidents for front line health care providers: a pilot study from Saudi Arabia. BMC MEDICAL EDUCATION 2022; 22:350. [PMID: 35534890 PMCID: PMC9082960 DOI: 10.1186/s12909-022-03427-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 05/03/2022] [Indexed: 05/30/2023]
Abstract
BACKGROUND Mass chemical exposure emergencies are infrequent but can cause injury, illness, or loss of life for large numbers of victims. These emergencies can stretch and challenge the available resources of healthcare systems within the community. Political unrest in the Middle East, including chemical terrorist attacks against civilians in Syria and increasing chemical industry accidents, have highlighted the lack of hospital preparedness for chemical incidents in the region. This study aimed to evaluate the effectiveness of a course designed to empower frontline healthcare providers involved in mass casualty incidents with the basic knowledge and essential operational skills for mass chemical exposure incidents in Saudi Arabia. METHODS A mixed-methods approach was used to develop a blended learning, simulation enhanced, competency-based course for major chemical incidents for front line healthcare providers. The course was designed by experts from different disciplines (disaster medicine, poisoning / toxicology, and Hazard Material Threat - HAZMAT team) in four stages. The course was piloted over five days at the Officers Club of the Ministry of Interior (Riyadh, Saudi Arabia). The 41 participants were from different government health discipline sectors in the country. Pre- and post-tests were used to assess learner knowledge while debriefing sessions after the decontamination triage session and simulation-enhanced exercises were used for team performance assessment. RESULTS The overall knowledge scores were significantly higher in the post-test (69.47%) than the pre-test (46.3%). All four knowledge domains also had significant differences between pre- and post-test results. There were no differences in the pre and post-test scores for healthcare providers from the different health disciplines. A one-year post-event survey demonstrated that participants were satisfied with their knowledge retention. Interestingly, 38.3% had the opportunity to put this knowledge into practice in relation to mass chemical exposure incidents. CONCLUSION Delivering a foundation level competency-based blended learning course with enhanced simulation training in major chemical incidents for front line healthcare providers may improve their knowledge and skills in response to such incidents. This in turn can improve the level of national preparedness and staff availability and make a crucial difference in reducing the health impacts among victims.
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Affiliation(s)
- Nidaa Bajow
- Security Forces Hospital Program, P O Box 89489, Riyadh, 11682, Kingdom of Saudi Arabia.
| | - Shahnaz Alkhalil
- Faculty of Engineering and Technology, Alzaytoonah University, Amman, Jordan
| | - Nisreen Maghraby
- King Fahad University Hospital Collage of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Kingdom of Saudi Arabia
| | - Saleh Alesa
- General Directorate of Medical Services Special Security Forces, Riyadh, Kingdom of Saudi Arabia
| | - Amal Al Najjar
- Security Forces Hospital Program, P O Box 89489, Riyadh, 11682, Kingdom of Saudi Arabia
| | - Samer Aloraifi
- Hail Health Cluster Ministry of Health, Hail, Kingdom of Saudi Arabia
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The Ability of Deep Learning Models to Identify Total Hip and Knee Arthroplasty Implant Design From Plain Radiographs. J Am Acad Orthop Surg 2022; 30:409-415. [PMID: 35139038 DOI: 10.5435/jaaos-d-21-00771] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 01/05/2022] [Indexed: 02/01/2023] Open
Abstract
INTRODUCTION The surgical management of patients with failed total hip or knee arthroplasty (THA and TKA) necessitates the identification of the implant manufacturer and model. Failure to accurately identify implant design leads to delays in care, increased morbidity, and healthcare costs. The automated identification of implant designs has the potential to assist in the surgical management of patients with failed arthroplasty. This study aimed to develop and validate a convolutional neural network deep learning model for the identification of primary and revision hip and knee total joint arthroplasty designs from plain radiographs. METHODS This study trained a convolutional neural network deep learning model to automatically identify 24 THA designs and 14 TKA designs from 11,204 anterior-posterior radiographs obtained from 8,763 patients. From these radiographs, 8,963 radiographs (80%) were used for model training and 2,241 radiographs (20%) were used for model validation. Model performance was assessed through receiver operating curve characteristics. RESULTS After 1,000 training epochs by the convolutional neural network deep learning model, the computational model discriminated 17 primary THA designs with an area under the receiver operating curve (AUC) of 0.98, sensitivity of 95.8%, and specificity of 98.6%. The deep learning model discriminated eight primary TKA designs with an AUC of 0.97, sensitivity of 94.9%, and specificity of 97.8%. The deep learning model demonstrated an AUC of 0.98 and 0.96 for the identification of seven revision THA and six revision TKA designs, respectively. DISCUSSION This study developed and validated a convolutional neural network deep learning model for the identification of hip and knee total joint arthroplasty designs from plain radiographs. The study findings demonstrate excellent accuracy of the deep learning model for the identification of 24 THA and 14 TKA designs, illustrating the great potential of the deep learning model to assist in preoperative surgical planning of failed arthroplasty patients.
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13
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Anderson MA. Clinical Issues-May 2022. AORN J 2022; 115:479-487. [PMID: 35476197 DOI: 10.1002/aorn.13674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 12/31/2021] [Indexed: 11/10/2022]
Abstract
Using the AORN staffing formula Key words: safe OR staffing, direct care, indirect care, on-call, full-time equivalents (FTEs). RN first assistant application prerequisites Key words: registered nurse first assistant (RNFA), certification, curriculum, education requirements, CNOR. Differences between preceptors and mentors Key words: novice nurse, expert, career development, professional growth, assimilation. Unique device identifier requirements Key words: global unique device identification database (GUDID), automatic identification and data capture (AIDC) technology, medical device, barcode, integration.
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14
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Schwartz JT, Valliani AA, Arvind V, Cho BH, Geng E, Henson P, Riew KD, Lehman RA, Lenke LG, Cho SK, Kim JS. Identification of Anterior Cervical Spinal Instrumentation Using a Smartphone Application Powered by Machine Learning. Spine (Phila Pa 1976) 2022; 47:E407-E414. [PMID: 34269759 DOI: 10.1097/brs.0000000000004172] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
STUDY DESIGN Cross-sectional study. OBJECTIVE The purpose of this study is to develop and validate a machine learning algorithm for the automated identification of anterior cervical discectomy and fusion (ACDF) plates from smartphone images of anterior-posterior (AP) cervical spine radiographs. SUMMARY OF BACKGROUND DATA Identification of existing instrumentation is a critical step in planning revision surgery for ACDF. Machine learning algorithms that are known to be adept at image classification may be applied to the problem of ACDF plate identification. METHODS A total of 402 smartphone images containing 15 different types of ACDF plates were gathered. Two hundred seventy-five images (∼70%) were used to train and validate a convolution neural network (CNN) for classification of images from radiographs. One hundred twenty-seven (∼30%) images were held out to test algorithm performance. RESULTS The algorithm performed with an overall accuracy of 94.4% and 85.8% for top-3 and top-1 accuracy, respectively. Overall positive predictive value, sensitivity, and f1-scores were 0.873, 0.858, and 0.855, respectively. CONCLUSION This algorithm demonstrates strong performance in the classification of ACDF plates from smartphone images and will be deployed as an accessible smartphone application for further evaluation, improvement, and eventual widespread use.Level of Evidence: 3.
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Affiliation(s)
- John T Schwartz
- Department of Orthopedic Surgery, Mount Sinai Health System, New York, NY
| | - Aly A Valliani
- Department of Orthopedic Surgery, Mount Sinai Health System, New York, NY
| | - Varun Arvind
- Department of Orthopedic Surgery, Mount Sinai Health System, New York, NY
| | - Brian H Cho
- Department of Orthopedic Surgery, Mount Sinai Health System, New York, NY
| | - Eric Geng
- Department of Orthopedic Surgery, Mount Sinai Health System, New York, NY
| | - Philip Henson
- Department of Orthopedic Surgery, Mount Sinai Health System, New York, NY
| | - K Daniel Riew
- Department of Orthopedic Surgery, Columbia University Medical Center, New York, NY
| | - Ronald A Lehman
- Department of Orthopedic Surgery, Columbia University Medical Center, New York, NY
| | - Lawrence G Lenke
- Department of Orthopedic Surgery, Columbia University Medical Center, New York, NY
| | - Samuel K Cho
- Department of Orthopedic Surgery, Mount Sinai Health System, New York, NY
| | - Jun S Kim
- Department of Orthopedic Surgery, Mount Sinai Health System, New York, NY
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15
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Application of deep learning algorithm in automated identification of knee arthroplasty implants from plain radiographs using transfer learning models: Are algorithms better than humans? J Orthop 2022; 32:139-145. [DOI: 10.1016/j.jor.2022.05.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/19/2022] [Accepted: 05/19/2022] [Indexed: 01/16/2023] Open
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16
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Applications of artificial intelligence and machine learning for the hip and knee surgeon: current state and implications for the future. INTERNATIONAL ORTHOPAEDICS 2022; 46:937-944. [PMID: 35171335 DOI: 10.1007/s00264-022-05346-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 02/05/2022] [Indexed: 12/17/2022]
Abstract
BACKGROUND Artificial Intelligence (AI)/Machine Learning (ML) applications have been proven efficient to improve diagnosis, to stratify risk, and to predict outcomes in many respective medical specialties, including in orthopaedics. CHALLENGES AND DISCUSSION Regarding hip and knee reconstruction surgery, AI/ML have not made it yet to clinical practice. In this review, we present sound AI/ML applications in the field of hip and knee degenerative disease and reconstruction. From osteoarthritis (OA) diagnosis and prediction of its advancement, clinical decision-making, identification of hip and knee implants to prediction of clinical outcome and complications following a reconstruction procedure of these joints, we report how AI/ML systems could facilitate data-driven personalized care for our patients.
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17
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AI MSK clinical applications: orthopedic implants. Skeletal Radiol 2022; 51:305-313. [PMID: 34350476 DOI: 10.1007/s00256-021-03879-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/15/2021] [Accepted: 07/22/2021] [Indexed: 02/02/2023]
Abstract
Artificial intelligence (AI) and deep learning have multiple potential uses in aiding the musculoskeletal radiologist in the radiological evaluation of orthopedic implants. These include identification of implants, characterization of implants according to anatomic type, identification of specific implant models, and evaluation of implants for positioning and complications. In addition, natural language processing (NLP) can aid in the acquisition of clinical information from the medical record that can help with tasks like prepopulating radiology reports. Several proof-of-concept works have been published in the literature describing the application of deep learning toward these various tasks, with performance comparable to that of expert musculoskeletal radiologists. Although much work remains to bring these proof-of-concept algorithms into clinical deployment, AI has tremendous potential toward automating these tasks, thereby augmenting the musculoskeletal radiologist.
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Wilson NA, Tcheng JE, Graham J, Drozda JP. Advancing Patient Safety Surrounding Medical Devices: A Health System Roadmap to Implement Unique Device Identification at the Point of Care. MEDICAL DEVICES-EVIDENCE AND RESEARCH 2021; 14:411-421. [PMID: 34880686 PMCID: PMC8645947 DOI: 10.2147/mder.s339232] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 11/10/2021] [Indexed: 12/13/2022] Open
Abstract
Background The US Food and Drug Administration’s Unique Device Identification System Rule of 2013 mandated manufacturers to assign unique device identifiers (UDIs) to their medical devices. Most high-risk (Class III), moderate-risk (Class II) and implantable devices now have UDIs. To achieve the necessary next step for a comprehensive UDI-enabled system for patient safety, UDIs must be electronically documented during patient care, a process not routinely done. The purpose of this research was to study the implementation experiences of diverse health systems in order to develop a roadmap for UDI implementation at the point of care. Methods Semi-structured interviews were conducted with personnel at health systems that had implemented UDI for implantable devices in their cardiac catheterization labs or operating rooms. Interviews were audio-recorded, transcribed, and analyzed using the framework methodology of Ritchie and Spencer. Data interpretation involved development of a conceptual model and detailed recommendations for UDI implementation. An expert panel evaluated and provided input on the roadmap. Results Twenty-four interviews at ten health systems were conducted by phone. Participants described implementation steps, factors and barriers impacting implementation. Findings populated a UDI implementation roadmap, that includes Foundational Themes, Key Components, Key Steps, UDI Use, and Outcomes. Conclusions and Implications The UDI implementation roadmap provides a framework for health systems to address the necessary steps and multilevel factors that underpin UDI implementation at the point of care. It is intended to guide and advance routine electronic documentation of UDIs for devices used during clinical care, the critical next step for a comprehensive UDI-enabled system to enhance medical device safety and effectiveness for patients.
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Affiliation(s)
- Natalia A Wilson
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
| | - James E Tcheng
- Duke University School of Medicine and Health System, Durham, NC, USA
| | - Jove Graham
- Center for Pharmacy Innovation and Outcomes, Geisinger, Danville, PA, USA
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19
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Sharma S, Batta V, Chidambaranathan M, Mathialagan P, Mani G, Kiruthika M, Datta B, Kamineni S, Reddy G, Masilamani S, Vijayan S, Amanatullah DF. Knee Implant Identification by Fine-Tuning Deep Learning Models. Indian J Orthop 2021; 55:1295-1305. [PMID: 34824729 PMCID: PMC8586384 DOI: 10.1007/s43465-021-00529-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 09/12/2021] [Indexed: 02/04/2023]
Abstract
BACKGROUND Identification of implant model from primary knee arthroplasty in pre-op planning of revision surgery is a challenging task with added delay. The direct impact of this inability to identify the implants in time leads to the increase in complexity in surgery. Deep learning in the medical field for diagnosis has shown promising results in getting better with every iteration. This study aims to find an optimal solution for the problem of identification of make and model of knee arthroplasty prosthesis using automated deep learning models. METHODS Deep learning algorithms were used to classify knee arthroplasty implant models. The training, validation and test comprised of 1078 radiographs with a total of 6 knee arthroplasty implant models with anterior-posterior (AP) and lateral views. The performance of the model was calculated using accuracy, sensitivity, and area under the receiver-operating characteristic curve (AUC), which were compared against multiple models trained for comparative in-depth analysis with saliency maps for visualization. RESULTS After training for a total of 30 epochs on all 6 models, the model performing the best obtained an accuracy of 96.38%, the sensitivity of 97.2% and AUC of 0.985 on an external testing dataset consisting of 162 radiographs. The best performing model correctly and uniquely identified the implants which could be visualized using saliency maps. CONCLUSION Deep learning models can be used to differentiate between 6 knee arthroplasty implant models. Saliency maps give us a better understanding of which regions the model is focusing on while predicting the results.
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Affiliation(s)
- Sukkrit Sharma
- Department of Computer Science and Engineering, School of Computing, SRM Institute of Science and Technology, Potheri, Kattankulathur, Chengalpattu District, Tamil Nadu 603203 India
| | - Vineet Batta
- Department of Orthopaedic, Luton and Dunstable University College London Hospitals NHS Foundation Trust, Luton, UK
| | - Malathy Chidambaranathan
- Department of Computer Science and Engineering, School of Computing, SRM Institute of Science and Technology, Potheri, Kattankulathur, Chengalpattu District, Tamil Nadu 603203 India
| | - Prabhakaran Mathialagan
- Department of Computer Science and Engineering, School of Computing, SRM Institute of Science and Technology, Potheri, Kattankulathur, Chengalpattu District, Tamil Nadu 603203 India
| | - Gayathri Mani
- Department of Computer Science and Engineering, School of Computing, SRM Institute of Science and Technology, Potheri, Kattankulathur, Chengalpattu District, Tamil Nadu 603203 India
| | - M. Kiruthika
- Department of Orthopaedic, Luton and Dunstable University College London Hospitals NHS Foundation Trust, Luton, UK
| | - Barun Datta
- Army Research and Referral, New Delhi, India
| | | | | | | | - Sandeep Vijayan
- Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Udupi, Karnataka India
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Dhalluin T, Fakhiri S, Bouzillé G, Herbert J, Rosset P, Cuggia M, Grammatico-Guillon L. Role of real-world digital data for orthopedic implant automated surveillance: a systematic review. Expert Rev Med Devices 2021; 18:799-810. [PMID: 34148465 DOI: 10.1080/17434440.2021.1943361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
INTRODUCTION Data collection automation through the reuse of real-world digital data from clinical data warehouses (CDW) could represent a great opportunity to improve medical device monitoring. For instance, this approach is starting to be used for the design of automated decision support systems for joint replacement monitoring. However, a number of obstacles remains, such as data quality and interoperability through the use of common and regularly updated terminologies, and the use of a Unique Device Identifier (UDI). AREAS COVERED To present the existing models of automated surveillance of orthopedic devices, a systematic review of initiatives using real-world digital health data to monitor joint replacement surgery was performed following the PRISMA 2020 guidelines. The main objective was to identify the data sources, the target populations, the population size, the device location, and the main results of studies on such initiatives. EXPERT OPINION Analysis of the identified studies showed that real-world digital data offer many opportunities for improving the automation of monitoring in orthopedics. The contribution of real-world data, especially through natural language processing, UDI use in CDW and the integration of device databases, is needed for automated and more robust health surveillance.
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Affiliation(s)
- Thibault Dhalluin
- Department of Medical Information, University Hospital of Tours, Tours, France. Medical School, University of Tours, EA, Tours, France
| | - Sara Fakhiri
- Department of Medical Information, University Hospital of Tours, Tours, France. Medical School, University of Tours, EA, Tours, France
| | | | - Julien Herbert
- Department of Medical Information, University Hospital of Tours, Tours, France. Medical School, University of Tours, EA, Tours, France
| | - Philippe Rosset
- Department of Orthopedic Surgery, University Hospital of Tours, Tours, France. Medical School, University of Tours, EA, Tours, France
| | - Marc Cuggia
- Univ Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, Rennes, France
| | - Leslie Grammatico-Guillon
- Department of Medical Information, University Hospital of Tours, Tours, France. Medical School, University of Tours, EA, Tours, France
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21
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Karnuta JM, Haeberle HS, Luu BC, Roth AL, Molloy RM, Nystrom LM, Piuzzi NS, Schaffer JL, Chen AF, Iorio R, Krebs VE, Ramkumar PN. Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Hip. J Arthroplasty 2021; 36:S290-S294.e1. [PMID: 33281020 DOI: 10.1016/j.arth.2020.11.015] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 09/30/2020] [Accepted: 11/08/2020] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND The surgical management of complications surrounding patients who have undergone hip arthroplasty necessitates accurate identification of the femoral implant manufacturer and model. Failure to do so risks delays in care, increased morbidity, and further economic burden. Because few arthroplasty experts can confidently classify implants using plain radiographs, automated image processing using deep learning for implant identification may offer an opportunity to improve the value of care rendered. METHODS We trained, validated, and externally tested a deep-learning system to classify total hip arthroplasty and hip resurfacing arthroplasty femoral implants as one of 18 different manufacturer models from 1972 retrospectively collected anterior-posterior (AP) plain radiographs from 4 sites in one quaternary referral health system. From these radiographs, 1559 were used for training, 207 for validation, and 206 for external testing. Performance was evaluated by calculating the area under the receiver-operating characteristic curve, sensitivity, specificity, and accuracy, as compared with a reference standard of implant model from operative reports with implant serial numbers. RESULTS The training and validation data sets from 1715 patients and 1766 AP radiographs included 18 different femoral components across four leading implant manufacturers and 10 fellowship-trained arthroplasty surgeons. After 1000 training epochs by the deep-learning system, the system discriminated 18 implant models with an area under the receiver-operating characteristic curve of 0.999, accuracy of 99.6%, sensitivity of 94.3%, and specificity of 99.8% in the external-testing data set of 206 AP radiographs. CONCLUSIONS A deep-learning system using AP plain radiographs accurately differentiated among 18 hip arthroplasty models from four industry leading manufacturers.
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Affiliation(s)
- Jaret M Karnuta
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH
| | - Heather S Haeberle
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH; Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Bryan C Luu
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH; Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, TX
| | - Alexander L Roth
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH
| | - Robert M Molloy
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH
| | - Lukas M Nystrom
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH
| | - Nicolas S Piuzzi
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH
| | | | - Antonia F Chen
- Department of Orthopaedic Surgery, Brigham & Women's Hospital, Boston, MA
| | - Richard Iorio
- Department of Orthopaedic Surgery, Brigham & Women's Hospital, Boston, MA
| | - Viktor E Krebs
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH
| | - Prem N Ramkumar
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH; Department of Orthopaedic Surgery, Brigham & Women's Hospital, Boston, MA
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Borjali A, Chen AF, Bedair HS, Melnic CM, Muratoglu OK, Morid MA, Varadarajan KM. Comparing the performance of a deep convolutional neural network with orthopedic surgeons on the identification of total hip prosthesis design from plain radiographs. Med Phys 2021; 48:2327-2336. [PMID: 33411949 DOI: 10.1002/mp.14705] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 12/24/2020] [Accepted: 12/27/2020] [Indexed: 01/09/2023] Open
Abstract
PURPOSE A crucial step in the preoperative planning for a revision total hip replacement (THR) surgery is the accurate identification of the failed implant design, especially if one or more well-fixed/functioning components are to be retained. Manual identification of the implant design from preoperative radiographic images can be time-consuming and inaccurate, which can ultimately lead to increased operating room time, more complex surgery, and increased healthcare costs. METHOD In this study, we present a novel approach to identifying THR femoral implants' design from plain radiographs using a convolutional neural network (CNN). We evaluated a total of 402 radiographs of nine different THR implant designs including, Accolade II (130 radiographs), Corail (89 radiographs), M/L Taper (31 radiographs), Summit (31 radiographs), Anthology (26 radiographs), Versys (26 radiographs), S-ROM (24 radiographs), Taperloc Standard Offset (24 radiographs), and Taperloc High Offset (21 radiographs). We implemented a transfer learning approach and adopted a DenseNet-201 CNN architecture by replacing the final classifier with nine fully connected neurons. Furthermore, we used saliency maps to explain the CNN decision-making process by visualizing the most important pixels in a given radiograph on the CNN's outcome. We also compared the CNN's performance with three board-certified and fellowship-trained orthopedic surgeons. RESULTS The CNN achieved the same or higher performance than at least one of the surgeons in identifying eight of nine THR implant designs and underperformed all of the surgeons in identifying one THR implant design (Anthology). Overall, the CNN achieved a lower Cohen's kappa (0.78) than surgeon 1 (1.00), the same Cohen's kappa as surgeon 2 (0.78), and a slightly higher Cohen's kappa than surgeon 3 (0.76) in identifying all the nine THR implant designs. Furthermore, the saliency maps showed that the CNN generally focused on each implant's unique design features to make a decision. Regarding the time spent performing the implant identification, the CNN accomplished this task in ~0.06 s per radiograph. The surgeon's identification time varied based on the method they utilized. When using their personal experience to identify the THR implant design, they spent negligible time. However, the identification time increased to an average of 8.4 min (standard deviation 6.1 min) per radiograph when they used another identification method (online search, consulting with the orthopedic company representative, and using image atlas), which occurred in about 17% of cases in the test subset (40 radiographs). CONCLUSIONS CNNs such as the one developed in this study can be used to automatically identify the design of a failed THR femoral implant preoperatively in just a fraction of a second, saving time and in some cases improving identification accuracy.
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Affiliation(s)
- Alireza Borjali
- Department of Orthopaedic, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, MA, USA
- Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA
| | - Antonia F Chen
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hany S Bedair
- Department of Orthopaedics, Massachusetts General Hospital, Boston, MA, USA
- Kaplan Joint Center, Department of Orthopedics, Newton-Wellesley Hospital, Newton, MA, USA
| | - Christopher M Melnic
- Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA
- Department of Orthopaedics, Massachusetts General Hospital, Boston, MA, USA
- Kaplan Joint Center, Department of Orthopedics, Newton-Wellesley Hospital, Newton, MA, USA
| | - Orhun K Muratoglu
- Department of Orthopaedic, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, MA, USA
- Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA
| | - Mohammad A Morid
- Department of Information Systems and Analytics, Santa Clara University Leavey School of Business, Santa Clara, CA, USA
| | - Kartik M Varadarajan
- Department of Orthopaedic, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, MA, USA
- Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA
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Patel R, Thong EHE, Batta V, Bharath AA, Francis D, Howard J. Automated Identification of Orthopedic Implants on Radiographs Using Deep Learning. Radiol Artif Intell 2021; 3:e200183. [PMID: 34350407 DOI: 10.1148/ryai.2021200183] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 02/21/2020] [Accepted: 03/01/2020] [Indexed: 12/15/2022]
Abstract
Accurate identification of metallic orthopedic implant design is important for preoperative planning of revision arthroplasty. Surgical records of implant models are frequently unavailable. The aim of this study was to develop and evaluate a convolutional neural network for identifying orthopedic implant models using radiographs. In this retrospective study, 427 knee and 922 hip unilateral anteroposterior radiographs, including 12 implant models from 650 patients, were collated from an orthopedic center between March 2015 and November 2019 to develop classification networks. A total of 198 images paired with autogenerated image masks were used to develop a U-Net segmentation network to automatically zero-mask around the implants on the radiographs. Classification networks processing original radiographs, and two-channel conjoined original and zero-masked radiographs, were ensembled to provide a consensus prediction. Accuracies of five senior orthopedic specialists assisted by a reference radiographic gallery were compared with network accuracy using McNemar exact test. When evaluated on a balanced unseen dataset of 180 radiographs, the final network achieved a 98.9% accuracy (178 of 180) and 100% top-three accuracy (180 of 180). The network performed superiorly to all five specialists (76.1% [137 of 180] median accuracy and 85.6% [154 of 180] best accuracy; both P < .001), with robustness to scan quality variation and difficult to distinguish implants. A neural network model was developed that outperformed senior orthopedic specialists at identifying implant models on radiographs; real-world application can now be readily realized through training on a broader range of implants and joints, supported by all code and radiographs being made freely available. Supplemental material is available for this article. Keywords: Neural Networks, Skeletal-Appendicular, Knee, Hip, Computer Applications-General (Informatics), Prostheses, Technology Assess-ment, Observer Performance © RSNA, 2021.
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Affiliation(s)
- Ravi Patel
- Faculty of Medicine, Imperial College Healthcare NHS Trust, London, England (R.P., E.H.E.T., D.F., J.H.); Department of Bioengineering, Imperial College London, Level 2, Faculty Building, South Kensington Campus, London SW7 2AZ, England (R.P., A.A.B.); and Department of Orthopaedic Surgery, Luton and Dunstable University Hospital, Luton, England (V.B.)
| | - Elizabeth H E Thong
- Faculty of Medicine, Imperial College Healthcare NHS Trust, London, England (R.P., E.H.E.T., D.F., J.H.); Department of Bioengineering, Imperial College London, Level 2, Faculty Building, South Kensington Campus, London SW7 2AZ, England (R.P., A.A.B.); and Department of Orthopaedic Surgery, Luton and Dunstable University Hospital, Luton, England (V.B.)
| | - Vineet Batta
- Faculty of Medicine, Imperial College Healthcare NHS Trust, London, England (R.P., E.H.E.T., D.F., J.H.); Department of Bioengineering, Imperial College London, Level 2, Faculty Building, South Kensington Campus, London SW7 2AZ, England (R.P., A.A.B.); and Department of Orthopaedic Surgery, Luton and Dunstable University Hospital, Luton, England (V.B.)
| | - Anil Anthony Bharath
- Faculty of Medicine, Imperial College Healthcare NHS Trust, London, England (R.P., E.H.E.T., D.F., J.H.); Department of Bioengineering, Imperial College London, Level 2, Faculty Building, South Kensington Campus, London SW7 2AZ, England (R.P., A.A.B.); and Department of Orthopaedic Surgery, Luton and Dunstable University Hospital, Luton, England (V.B.)
| | - Darrel Francis
- Faculty of Medicine, Imperial College Healthcare NHS Trust, London, England (R.P., E.H.E.T., D.F., J.H.); Department of Bioengineering, Imperial College London, Level 2, Faculty Building, South Kensington Campus, London SW7 2AZ, England (R.P., A.A.B.); and Department of Orthopaedic Surgery, Luton and Dunstable University Hospital, Luton, England (V.B.)
| | - James Howard
- Faculty of Medicine, Imperial College Healthcare NHS Trust, London, England (R.P., E.H.E.T., D.F., J.H.); Department of Bioengineering, Imperial College London, Level 2, Faculty Building, South Kensington Campus, London SW7 2AZ, England (R.P., A.A.B.); and Department of Orthopaedic Surgery, Luton and Dunstable University Hospital, Luton, England (V.B.)
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Karnuta JM, Luu BC, Roth AL, Haeberle HS, Chen AF, Iorio R, Schaffer JL, Mont MA, Patterson BM, Krebs VE, Ramkumar PN. Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Knee. J Arthroplasty 2021; 36:935-940. [PMID: 33160805 DOI: 10.1016/j.arth.2020.10.021] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 09/20/2020] [Accepted: 10/13/2020] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Revisions and reoperations for patients who have undergone total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), and distal femoral replacement (DFR) necessitates accurate identification of implant manufacturer and model. Failure risks delays in care, increased morbidity, and further financial burden. Deep learning permits automated image processing to mitigate the challenges behind expeditious, cost-effective preoperative planning. Our aim was to investigate whether a deep-learning algorithm could accurately identify the manufacturer and model of arthroplasty implants about the knee from plain radiographs. METHODS We trained, validated, and externally tested a deep-learning algorithm to classify knee arthroplasty implants from one of 9 different implant models from retrospectively collected anterior-posterior (AP) plain radiographs from four sites in one quaternary referral health system. The performance was evaluated by calculating the area under the receiver-operating characteristic curve (AUC), sensitivity, specificity, and accuracy when compared with a reference standard of implant model from operative reports. RESULTS The training and validation data sets were comprised of 682 radiographs across 424 patients and included a wide range of TKAs from the four leading implant manufacturers. After 1000 training epochs by the deep-learning algorithm, the model discriminated nine implant models with an AUC of 0.99, accuracy 99%, sensitivity of 95%, and specificity of 99% in the external-testing data set of 74 radiographs. CONCLUSIONS A deep learning algorithm using plain radiographs differentiated between 9 unique knee arthroplasty implants from four manufacturers with near-perfect accuracy. The iterative capability of the algorithm allows for scalable expansion of implant discriminations and represents an opportunity in delivering cost-effective care for revision arthroplasty.
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Affiliation(s)
- Jaret M Karnuta
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH
| | - Bryan C Luu
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH; Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, TX
| | - Alexander L Roth
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH
| | - Heather S Haeberle
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH; Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Antonia F Chen
- Department of Orthopaedic Surgery, Brigham & Women''s Hospital, Boston, MA
| | - Richard Iorio
- Department of Orthopaedic Surgery, Brigham & Women''s Hospital, Boston, MA
| | | | - Michael A Mont
- Department of Orthopaedic Surgery, Lenox Hill Hospital, Northwell Health, New York, NY
| | | | - Viktor E Krebs
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH
| | - Prem N Ramkumar
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH; Department of Orthopaedic Surgery, Brigham & Women''s Hospital, Boston, MA
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25
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Belete SC, Batta V, Kunz H. Automated classification of total knee replacement prosthesis on plain film radiograph using a deep convolutional neural network. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100669] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
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26
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Ghose S, Datta S, Batta V, Malathy C, M G. Artificial Intelligence based identification of Total Knee Arthroplasty Implants. 2020 3RD INTERNATIONAL CONFERENCE ON INTELLIGENT SUSTAINABLE SYSTEMS (ICISS) 2020. [DOI: 10.1109/iciss49785.2020.9315956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
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27
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Borjali A, Chen AF, Muratoglu OK, Morid MA, Varadarajan KM. Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network. J Orthop Res 2020; 38:1465-1471. [PMID: 31997411 DOI: 10.1002/jor.24617] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 09/12/2020] [Accepted: 01/13/2020] [Indexed: 02/04/2023]
Abstract
Identifying the design of a failed implant is a key step in the preoperative planning of revision total joint arthroplasty. Manual identification of the implant design from radiographic images is time-consuming and prone to error. Failure to identify the implant design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in the identification of three commonly used THR implant designs. Such CNN can be used to automatically identify the design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners' time, and reduce healthcare costs.
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Affiliation(s)
- Alireza Borjali
- Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, Massachusetts.,Department of Orthopaedic Surgery, Harvard Medical School, Boston, Massachusetts
| | - Antonia F Chen
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Orhun K Muratoglu
- Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, Massachusetts.,Department of Orthopaedic Surgery, Harvard Medical School, Boston, Massachusetts
| | - Mohammad A Morid
- Department of Information Systems and Analytics, Santa Clara University Leavey School of Business, Santa Clara, California
| | - Kartik M Varadarajan
- Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, Massachusetts.,Department of Orthopaedic Surgery, Harvard Medical School, Boston, Massachusetts
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28
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Abstract
The US Food and Drug Administration's 2013 Unique Device Identification System Rule requires manufacturers to label devices with unique identifiers. Implantable devices are now shipped with unique identifiers, and many electronic health records have fields to incorporate them. Health policy changes have prompted hospital systems to assess implementation of implant barcode scanning systems to capture unique device identifiers. Project aims were to assess predictors of operating room nurses' acceptance of a new implant barcode scanning system, describe operating room nurses' perceptions of the system value, and identify operating room nurses' perceived gaps in system implementation. An online survey was disseminated to operating room nurses, and focus groups were conducted with orthopedic operating room nurses in an academic medical center that had recently implemented an implant barcode scanning system in surgical services. Predictors of barcode scanning acceptance included perceived usefulness for patient care, perceived ease of use, and perceived usefulness (self). Nurses perceived the system to be more accurate and valuable for patient safety. Perceived gaps in system implementation related to communication, completeness of the system, consistency in process, and training. Understanding nurse perceptions of new barcode scanning systems and engaging them in the implementation process are key areas for success and optimization of these systems.
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29
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Bayrak T, Özdiler Çopur F. Evaluation of the unique device identification system and an approach for medical device tracking. HEALTH POLICY AND TECHNOLOGY 2017. [DOI: 10.1016/j.hlpt.2017.04.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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