National projections of time, cost and failure in implantable device identification: Consideration of unique device identification use

Wilson, Natalia; Broatch, Jennifer; Jehn, Megan; Davis, Charles M.

doi:10.1016/j.hjdsi.2015.04.003

Cited by 38 publications

(24 citation statements)

References 19 publications

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“…Furthermore, in an estimated 10% of the cases surgeons could not identify the device preoperatively, and in approximately 2% of cases, they failed to identify the device intraoperatively. The failure to identify the implant preoperatively was perceived to result in greater than or equal to two implants brought to the case, increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and increased healthcare costs 1,2 . This demonstrates both the inefficiencies associated with routine identification of failed components prior to revision arthroplasty, as well as the potentially significant impact on patient health and overall cost of care when such identification cannot be accomplished.…”

Section: Introductionmentioning

confidence: 99%

Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network

Borjali

Chen

Muratoglu

et al. 2020

Journal Orthopaedic Research

104

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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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Section: Introductionmentioning

confidence: 99%

Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network

Borjali

Chen

Muratoglu

et al. 2020

Journal Orthopaedic Research

104

View full text Add to dashboard Cite

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“…2 The survey respondents reported that in about one of ten cases, they could not identify the implant preoperatively, which then led to additional requested implants, added surgical time/complexity, increased blood/ bone loss, and increased recovery time for patients. 2,3 In another study, the authors calculated the opportunity cost associated with time spent determining the implant design preoperatively based on the AAHKS survey. 2 Their projection suggested that cumulative surgeon time spent identifying failed implants could reach 133 000 h in 2030, costing the healthcare system about $27.4 million based on surgeon Medicare reimbursement (2014 coding dollars) rate.…”

Section: Introductionmentioning

confidence: 99%

Comparing the performance of a deep convolutional neural network with orthopedic surgeons on the identification of total hip prosthesis design from plain radiographs

et al. 2021

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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 wellfixed/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).

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“…They reported using five or more methods to identify the implant including radiographs, hospital records, office records, primary procedure notes and implant sheets 1 . The survey respondents reported that in about 1/10 cases, they could not identify the implant pre-operatively, which then lead to additional requested implants, added surgical time/complexity, increased blood/bone loss, and increased recovery time for patients 1,2 . In another study, the authors calculated the opportunity cost associated with time spent determining the implant design preoperatively based on the AAHKS survey 2 . Their projection suggested that cumulative surgeon time spent identifying failed implants could reach 133,000 hours in 2030, costing the healthcare system about $27.4 million based on surgeon Medicare reimbursement (2014 coding dollars) rate.…”

Section: Introductionmentioning

confidence: 99%

“…Their projection suggested that cumulative surgeon time spent identifying failed implants could reach 133,000 hours in 2030, costing the healthcare system about $27.4 million based on surgeon Medicare reimbursement (2014 coding dollars) rate. Furthermore, the authors projected that by 2030, up to 50,000 cases may not preoperatively identify the failed implant and in about 25,000 cases, the failed components may not be identified intraoperatively 2 . When the failed components cannot be identified, all components including well-fixed/functioning components need to be unnecessarily removed to avoid issues due to mismatching components of different THR implant designs.…”

Section: Introductionmentioning

confidence: 99%

“…The use of non-standardized and manual methods for documentation have been cited as a major barrier in accurate device identification 1 . Important regulatory changes have occurred in an effort to improve implant identification and traceability, including the Food and Drug Administration’s (FDA) rule requiring manufacturers to label medical devices with unique device identifiers (UDIs), establishment of publicly accessible Global Unique Device Identifier Database (GUDID) 2 , and the mandated inclusion of UDI in the patients’ medical records by the Office of the National Coordinator (ONC) for Health Information Technology and Centers for Medicare and Medicaid Services (CMS) 3,4 . However, as of 2014, over 10 million patients in the United States (US) are living with a hip or knee replacement 5 , predating these regulatory changes.…”

Section: Introductionmentioning

confidence: 99%

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Comparing performance of deep convolutional neural network with orthopaedic surgeons on identification of total hip prosthesis design from plain radiographs

Borjali

Chen

Bedair

et al. 2020

Preprint

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A crucial step in preoperative planning for a revision total hip replacement (THR) surgery is accurate identification of 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. No automated system has been developed to accurately and efficiently identify THR implant designs. In this study, we present a novel, fully automatic and interpretable approach to identify the design of nine different THR femoral implants from plain radiographs using deep convolutional neural network (CNN). We also compared the CNN performance with three board-certified and fellowship trained orthopaedic surgeons. The CNN achieved on-par accuracy with the orthopaedic surgeons while being significantly faster. The need for additional training data for less distinct designs was also highlighted. Such CNN 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 improving identification accuracy.

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National projections of time, cost and failure in implantable device identification: Consideration of unique device identification use

Cited by 38 publications

References 19 publications

Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network

Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network

Comparing the performance of a deep convolutional neural network with orthopedic surgeons on the identification of total hip prosthesis design from plain radiographs

Comparing performance of deep convolutional neural network with orthopaedic surgeons on identification of total hip prosthesis design from plain radiographs

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