+1 Recommend
0 collections
      • Record: found
      • Abstract: found
      • Article: not found

      Modern Internet Search Analytics and Total Joint Arthroplasty: What Are Patients Asking and Reading Online?


      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.



          Patients considering total joint arthroplasty often search for information online regarding surgery, however little is known about the specific topics that patients search for and the nature of the information provided. Google compiles frequently asked questions associated with a search term using machine learning and natural language processing. Links to individual websites are provided to answer each question. Analysis of this data may help improve understanding of patient concerns and inform more effective counseling.


          Search terms were entered into Google for total hip and total knee arthroplasty. Frequently asked questions and associated websites were extracted to a database using customized software. Questions were categorized by topic; websites were categorized by type. JAMA Benchmark Criteria were used to assess website quality. Pearson’s chi-square and Student’s t-tests were performed as appropriate.


          A total of 620 questions (305 TKA, 315 THA) were extracted with 602 associated websites. The most popular question topics were Specific Activities (23.5%), Indications/Management (15.6%), and Restrictions (13.4%). Questions related to Pain were more common in the TKA group (23.0% vs 2.5%, p<0.001) compared to THA. The most common website types were Academic (31.1%), Commercial (29.2%), and Social Media (17.1%). JAMA scores (0-4) were highest for Government websites (mean 3.92, p=0.005).


          The most frequently asked questions on Google related to total joint arthroplasty are related to arthritis management, rehabilitation, and ability to perform specific tasks. A sizeable proportion of health information provided originate from non-academic, non-government sources (64.4%), with 17.1% from social media websites.

          Related collections

          Most cited references40

          • Record: found
          • Abstract: found
          • Article: not found

          Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

          Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.
            • Record: found
            • Abstract: found
            • Article: not found

            Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT

            Background Coronavirus disease has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances. Materials and Methods In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19. Community acquired pneumonia (CAP) and other non-pneumonia CT exams were included to test the robustness of the model. The datasets were collected from 6 hospitals between August 2016 and February 2020. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results The collected dataset consisted of 4356 chest CT exams from 3,322 patients. The average age is 49±15 years and there were slightly more male patients than female (1838 vs 1484; p-value=0.29). The per-exam sensitivity and specificity for detecting COVID-19 in the independent test set was 114 of 127 (90% [95% CI: 83%, 94%]) and 294 of 307 (96% [95% CI: 93%, 98%]), respectively, with an AUC of 0.96 (p-value<0.001). The per-exam sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175) and 92% (239 of 259), respectively, with an AUC of 0.95 (95% CI: 0.93, 0.97). Conclusions A deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases.
              • Record: found
              • Abstract: not found
              • Article: not found

              Artificial intelligence in healthcare


                Author and article information

                J Arthroplasty
                J Arthroplasty
                The Journal of Arthroplasty
                Elsevier Inc.
                20 October 2020
                20 October 2020
                [1 ]Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
                [2 ]Department of Orthopaedic Surgery, New York-Presbyterian Hospital, Weill Cornell Medical Center, 525 East 68th Street, New York, NY 10065, USA
                Author notes
                []Corresponding author: Tony S. Shen, MD Hospital for Special Surgery 535 East 70th Street New York, NY 10021, USA
                © 2020 Elsevier Inc. All rights reserved.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                : 23 July 2020
                : 3 October 2020
                : 14 October 2020

                total hip arthroplasty,total knee arthroplasty,google,search analytics,machine learning,natural language processing,online health information


                Comment on this article