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DOI . ORG {}

  1. Analyzed Page
  2. Matching Content Categories
  3. CMS
  4. Monthly Traffic Estimate
  5. How Does Doi.org Make Money
  6. Keywords
  7. Topics
  8. Questions
  9. Schema
  10. External Links
  11. Analytics And Tracking
  12. Libraries
  13. Hosting Providers
  14. CDN Services

We began analyzing https://link.springer.com/article/10.1007/s10278-017-9969-2, but it redirected us to https://link.springer.com/article/10.1007/s10278-017-9969-2. The analysis below is for the second page.

Title[redir]:
PathBot: A Radiology-Pathology Correlation Dashboard | Journal of Imaging Informatics in Medicine
Description:
Pathology is considered the “gold standard” of diagnostic medicine. The importance of radiology-pathology correlation is seen in interdepartmental patient conferences such as “tumor boards” and by the tradition of radiology resident immersion in a radiologic-pathology course at the American Institute of Radiologic Pathology. In practice, consistent pathology follow-up can be difficult due to time constraints and cumbersome electronic medical records. We present a radiology-pathology correlation dashboard that presents radiologists with pathology reports matched to their dictations, for both diagnostic imaging and image-guided procedures. In creating our dashboard, we utilized the RadLex ontology and National Center for Biomedical Ontology (NCBO) Annotator to identify anatomic concepts in pathology reports that could subsequently be mapped to relevant radiology reports, providing an automated method to match related radiology and pathology reports. Radiology-pathology matches are presented to the radiologist on a web-based dashboard. We found that our algorithm was highly specific in detecting matches. Our sensitivity was slightly lower than expected and could be attributed to missing anatomy concepts in the RadLex ontology, as well as limitations in our parent term hierarchical mapping and synonym recognition algorithms. By automating radiology-pathology correlation and presenting matches in a user-friendly dashboard format, we hope to encourage pathology follow-up in clinical radiology practice for purposes of self-education and to augment peer review. We also hope to provide a tool to facilitate the production of quality teaching files, lectures, and publications. Diagnostic images have a richer educational value when they are backed up by the gold standard of pathology.

Matching Content Categories {📚}

  • Education
  • Social Networks
  • Video & Online Content

Content Management System {📝}

What CMS is doi.org built with?

Custom-built

No common CMS systems were detected on Doi.org, and no known web development framework was identified.

Traffic Estimate {📈}

What is the average monthly size of doi.org audience?

🌠 Phenomenal Traffic: 5M - 10M visitors per month


Based on our best estimate, this website will receive around 5,000,019 visitors per month in the current month.
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How Does Doi.org Make Money? {💸}

We can't tell how the site generates income.

Earning money isn't the goal of every website; some are designed to offer support or promote social causes. People have different reasons for creating websites. This might be one such reason. Doi.org could be secretly minting cash, but we can't detect the process.

Keywords {🔍}

article, radiology, google, scholar, pubmed, dashboard, imaging, pathology, radiologypathology, correlation, journal, filice, privacy, cookies, content, data, kelahan, kalaria, reports, radiol, publish, search, radlex, automated, tool, access, coll, doijjacr, usage, information, log, research, digital, linda, ross, diagnostic, medical, ontology, method, related, matches, review, quality, teaching, institution, chapter, discover, accessed, march, roentgenol,

Topics {✒️}

gov/radiation-emittingproducts/mammographyqualitystandardsactandprogram/regulations/ucm110823 quality teaching files radiology-pathology correlation dashboard automating radiology-pathology correlation month download article/chapter user-friendly dashboard format radiology-pathology correlation natural language processing match related radiology radiology report comparator radiology-pathology matches radiology resident immersion radiology panel discussion academic radiology departments privacy choices/manage cookies trainee report dashboard full article pdf digital imaging aims relevant radiology reports consistent pathology follow encourage pathology follow clinical radiology practice web-based dashboard call case dashboard semantic search–enabled pathology reports matched automated biopsy follow open biomedical annotator institutional review board discrepant imaging findings gov/pubmed/21347171 european economic area interdepartmental patient conferences image-guided procedures identify anatomic concepts missing anatomy concepts synonym recognition algorithms augment peer review peer review process tracking r1 exposure high-acuity cases breast imaging fellows check access instant access conditions privacy policy related subjects accepting optional cookies academic chief residents gorniak rjt augment resident education

Questions {❓}

  • Masters of radiology panel discussion: defining a quality dashboard for radiology—what are the right metrics?
  • Verma N, Hippe DS, Robinson JD: JOURNAL CLUB: assessment of interobserver variability in the peer review process: should we agree to disagree?

Schema {🗺️}

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         headline:PathBot: A Radiology-Pathology Correlation Dashboard
         description:Pathology is considered the “gold standard” of diagnostic medicine. The importance of radiology-pathology correlation is seen in interdepartmental patient conferences such as “tumor boards” and by the tradition of radiology resident immersion in a radiologic-pathology course at the American Institute of Radiologic Pathology. In practice, consistent pathology follow-up can be difficult due to time constraints and cumbersome electronic medical records. We present a radiology-pathology correlation dashboard that presents radiologists with pathology reports matched to their dictations, for both diagnostic imaging and image-guided procedures. In creating our dashboard, we utilized the RadLex ontology and National Center for Biomedical Ontology (NCBO) Annotator to identify anatomic concepts in pathology reports that could subsequently be mapped to relevant radiology reports, providing an automated method to match related radiology and pathology reports. Radiology-pathology matches are presented to the radiologist on a web-based dashboard. We found that our algorithm was highly specific in detecting matches. Our sensitivity was slightly lower than expected and could be attributed to missing anatomy concepts in the RadLex ontology, as well as limitations in our parent term hierarchical mapping and synonym recognition algorithms. By automating radiology-pathology correlation and presenting matches in a user-friendly dashboard format, we hope to encourage pathology follow-up in clinical radiology practice for purposes of self-education and to augment peer review. We also hope to provide a tool to facilitate the production of quality teaching files, lectures, and publications. Diagnostic images have a richer educational value when they are backed up by the gold standard of pathology.
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      description:Pathology is considered the “gold standard” of diagnostic medicine. The importance of radiology-pathology correlation is seen in interdepartmental patient conferences such as “tumor boards” and by the tradition of radiology resident immersion in a radiologic-pathology course at the American Institute of Radiologic Pathology. In practice, consistent pathology follow-up can be difficult due to time constraints and cumbersome electronic medical records. We present a radiology-pathology correlation dashboard that presents radiologists with pathology reports matched to their dictations, for both diagnostic imaging and image-guided procedures. In creating our dashboard, we utilized the RadLex ontology and National Center for Biomedical Ontology (NCBO) Annotator to identify anatomic concepts in pathology reports that could subsequently be mapped to relevant radiology reports, providing an automated method to match related radiology and pathology reports. Radiology-pathology matches are presented to the radiologist on a web-based dashboard. We found that our algorithm was highly specific in detecting matches. Our sensitivity was slightly lower than expected and could be attributed to missing anatomy concepts in the RadLex ontology, as well as limitations in our parent term hierarchical mapping and synonym recognition algorithms. By automating radiology-pathology correlation and presenting matches in a user-friendly dashboard format, we hope to encourage pathology follow-up in clinical radiology practice for purposes of self-education and to augment peer review. We also hope to provide a tool to facilitate the production of quality teaching files, lectures, and publications. Diagnostic images have a richer educational value when they are backed up by the gold standard of pathology.
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External Links {🔗}(105)

Analytics and Tracking {📊}

  • Google Tag Manager

Libraries {📚}

  • Clipboard.js
  • Prism.js

Emails and Hosting {✉️}

Mail Servers:

  • mx.zoho.eu
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Name Servers:

  • josh.ns.cloudflare.com
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CDN Services {📦}

  • Crossref

8.74s.