The current utilization of the patient-reported outcome measurement information system (PROMIS) in isolated or combined total knee arthroplasty populations

Gupta, Puneet; Czerwonka, Natalia; Desai, Sohil S.; deMeireles, Alirio J.; Trofa, David P.; Neuwirth, Alexander L.

doi:10.1186/s43019-023-00177-3

Cited by 9 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main purpose of TKA is to relieve the patient’s pain and restore function for a long time [ 37 ]. It is well known as the most effective and satisfactory surgical treatment in the field of orthopedic surgery and has a very good survival rate [ 38 , 39 , 40 , 41 , 42 ]. The 15-year survival rate is 93.0%; the 20-year survival rate is 90.1%; and the 25-year survival rate is reported to be 82.3% [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

“…The 15-year survival rate is 93.0%; the 20-year survival rate is 90.1%; and the 25-year survival rate is reported to be 82.3% [ 37 ]. The result of such excellent long-term survival rates was made possible by the development of artificial joint materials and continuous technology [ 39 ]. Unfortunately, artificial joints always have a risk of failure due to various reasons [ 14 ].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning for Detecting Total Knee Arthroplasty Implant Loosening on Plain Radiographs

et al. 2023

View full text Add to dashboard Cite

(1) Background: The purpose of this study was to investigate whether the loosening of total knee arthroplasty (TKA) implants could be detected accurately on plain radiographs using a deep convolution neural network (CNN). (2) Methods: We analyzed data for 100 patients who underwent revision TKA due to prosthetic loosening at a single institution from 2012 to 2020. We extracted 100 patients who underwent primary TKA without loosening through a propensity score, matching for age, gender, body mass index, operation side, and American Society of Anesthesiologists class. Transfer learning was used to prepare a detection model using a pre-trained Visual Geometry Group (VGG) 19. For transfer learning, two methods were used. First, the fully connected layer was removed, and a new fully connected layer was added to construct a new model. The convolutional layer was frozen without training, and only the fully connected layer was trained (transfer learning model 1). Second, a new model was constructed by adding a fully connected layer and varying the range of freezing for the convolutional layer (transfer learning model 2). (3) Results: The transfer learning model 1 gradually increased in accuracy and ultimately reached 87.5%. After processing through the confusion matrix, the sensitivity was 90% and the specificity was 100%. Transfer learning model 2, which was trained on the convolutional layer, gradually increased in accuracy and ultimately reached 97.5%, which represented a better improvement than for model 1. Processing through the confusion matrix affirmed that the sensitivity was 100% and the specificity was 97.5%. (4) Conclusions: The CNN algorithm, through transfer learning, shows high accuracy for detecting the loosening of TKA implants on plain radiographs.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Machine Learning for Detecting Total Knee Arthroplasty Implant Loosening on Plain Radiographs

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In addition, long-term follow-up results have shown excellent survival rates [9,10]. The development of materials and technologies related to THA and TKA has contributed to the increase in the number of THA and TKA worldwide [41,42]. However, artificial joints inevitably have the risk of revision operation; thus, the most common cause of revision operation is loosening due to osteolysis [16,17].…”

Section: Discussionmentioning

confidence: 99%

Detection of Prosthetic Loosening in Hip and Knee Arthroplasty Using Machine Learning: A Systematic Review and Meta-Analysis

Kim

Kang

et al. 2023

Medicina

View full text Add to dashboard Cite

Background: prosthetic loosening after hip and knee arthroplasty is one of the most common causes of joint arthroplasty failure and revision surgery. Diagnosis of prosthetic loosening is a difficult problem and, in many cases, loosening is not clearly diagnosed until accurately confirmed during surgery. The purpose of this study is to conduct a systematic review and meta-analysis to demonstrate the analysis and performance of machine learning in diagnosing prosthetic loosening after total hip arthroplasty (THA) and total knee arthroplasty (TKA). Materials and Methods: three comprehensive databases, including MEDLINE, EMBASE, and the Cochrane Library, were searched for studies that evaluated the detection accuracy of loosening around arthroplasty implants using machine learning. Data extraction, risk of bias assessment, and meta-analysis were performed. Results: five studies were included in the meta-analysis. All studies were retrospective studies. In total, data from 2013 patients with 3236 images were assessed; these data involved 2442 cases (75.5%) with THAs and 794 cases (24.5%) with TKAs. The most common and best-performing machine learning algorithm was DenseNet. In one study, a novel stacking approach using a random forest showed similar performance to DenseNet. The pooled sensitivity across studies was 0.92 (95% CI 0.84–0.97), the pooled specificity was 0.95 (95% CI 0.93–0.96), and the pooled diagnostic odds ratio was 194.09 (95% CI 61.60–611.57). The I2 statistics for sensitivity and specificity were 96% and 62%, respectively, showing that there was significant heterogeneity. The summary receiver operating characteristics curve indicated the sensitivity and specificity, as did the prediction regions, with an AUC of 0.9853. Conclusions: the performance of machine learning using plain radiography showed promising results with good accuracy, sensitivity, and specificity in the detection of loosening around THAs and TKAs. Machine learning can be incorporated into prosthetic loosening screening programs.

show abstract

“…These evaluation tools all have the benefit of coming from the patient perspective and have been widely reported in the literature. These scoring tools have been involved in improving clinical practice in their respective subspecialties through quantifying patients’ perceptions of their physical function and standardized reporting of patient outcomes 29–31 . However, these scoring tools are not specific for the musculoskeletal oncology patient population.…”

Section: Orthopedic Oncology‐specific Measuresmentioning

confidence: 99%

Patient‐reported outcome tools in musculoskeletal oncology

Vijayakumar

Blank

2023

Journal of Surgical Oncology

View full text Add to dashboard Cite

Patient‐reported outcomes (PRO) are collected directly from the patient and have become increasingly utilized in the clinical setting and in clinical research. In musculoskeletal oncology patients, a number of patient‐reported outcomes measures (PROM) have been developed and investigated to evaluate functional outcomes and health‐related quality of life in these patients. With the growing evidence for PROM in musculoskeletal oncology patients, PROM should be considered for the clinical care of these patients.

show abstract

The current utilization of the patient-reported outcome measurement information system (PROMIS) in isolated or combined total knee arthroplasty populations

Cited by 9 publications

References 31 publications

Machine Learning for Detecting Total Knee Arthroplasty Implant Loosening on Plain Radiographs

Machine Learning for Detecting Total Knee Arthroplasty Implant Loosening on Plain Radiographs

Detection of Prosthetic Loosening in Hip and Knee Arthroplasty Using Machine Learning: A Systematic Review and Meta-Analysis

Patient‐reported outcome tools in musculoskeletal oncology

Contact Info

Product

Resources

About