با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
Surgical Phase Recognition Method with a Sequential Consistency for CAOS-AI Navigation System
روش تشخیص مرحله جراحی با یک سازگاری متوالی برای سیستم ناوبری CAOS-AI-2020 The procedure of orthopedic surgery is quite
complicated, and many kinds of equipment have been used.
Operating room nurses who deliver surgical instruments to surgeon
are supposed to be forced to incur a heavy burden. There are some
studies to recognize surgical phase with convolutional neural
network (CNN) in minimally invasive laparoscopic surgery only.
Previously, we proposed a computer-aided orthopedic surgery
(CAOS)-AI navigation system based on CNN. However, the work
propose a method to improve accuracy of phase recognition by
considering temporal dependency of orthopedic surgery video
acquired from surgeon-wearable video camera. The method
estimates current surgical phase by combining both temporal
dependency and convolutional-long-short term memory network
(CNN-LSTM). Experimental results shows a phase recognition
accuracy of 59.9% by the proposed method applied in unicomapartmenatal
knee arthroplasty (UKA). Keywords: Deep Learning | Computer-aided Orthopaedic Surgery | Operating Room Nurse | Phase Recognition |
مقاله انگلیسی |
2 |
Development of Machine Learning Algorithms for Prediction of Sustained Postoperative Opioid Prescriptions After Total Hip Arthroplasty
توسعه الگوریتم های یادگیری ماشین برای پیش بینی نسخه های افیونی پس از عمل پایدار پس از آرتروپلاستی کامل باسن-2019 Background: Postoperative recovery after total hip arthroplasty (THA) can lead to the development of
prolonged opioid use but there are few tools for predicting this adverse outcome. The purpose of this
study is to develop machine learning algorithms for preoperative prediction of prolonged opioid prescriptions
after THA.
Methods: A retrospective review of electronic health records was conducted at 2 academic medical
centers and 3 community hospitals to identify adult patients who underwent THA for osteoarthritis
between January 1, 2000 and August 1, 2018. Prolonged postoperative opioid prescriptions were defined
as continuous opioid prescriptions after surgery to at least 90 days after surgery. Five machine learning
algorithms were developed to predict this outcome and were assessed by discrimination, calibration, and
decision curve analysis.
Results: Overall, 5507 patients underwent THA, of which 345 (6.3%) had prolonged postoperative opioid
prescriptions. The factors determined for prediction of prolonged postoperative opioid prescriptions
were age, duration of opioid exposure, preoperative hemoglobin, and preoperative medications (antidepressants,
benzodiazepines, nonsteroidal anti-inflammatory drugs, and beta-2-agonists). The elasticnet
penalized logistic regression model achieved the best performance across discrimination
(c-statistic ¼ 0.77), calibration, and decision curve analysis. This model was incorporated into a digital
application able to provide both predictions and explanations (available at https://sorg-apps.shinyapps.
io/thaopioid/).
Conclusion: If externally validated in independent populations, the algorithms developed in this study
could improve preoperative screening and support for THA patients at high risk for prolonged postoperative
opioid prescriptions. Early identification and intervention in high-risk cases may mitigate the
long-term adverse consequence of opioid dependence.
Level of Evidence: III. Keywords: arthroplasty | machine learning | opioid use | orthopedic surgery | prediction | total hip arthroplasty |
مقاله انگلیسی |