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optical character recognition  (IQVIA Inc)

 
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    Structured Review

    IQVIA Inc optical character recognition
    Key terms related to machine learning in clinical research
    Optical Character Recognition, supplied by IQVIA Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/optical+character+recognition+function/optical+character+recognition/pmc08365941-116-23-2
    Average 90 stars, based on 1 article reviews
    optical character recognition - by Bioz Stars, 2026-10
    90/100 stars

    Images

    1) Product Images from "The role of machine learning in clinical research: transforming the future of evidence generation"

    Article Title: The role of machine learning in clinical research: transforming the future of evidence generation

    Journal: Trials

    doi: 10.1186/s13063-021-05489-x

    Key terms related to machine learning in clinical research
    Figure Legend Snippet: Key terms related to machine learning in clinical research

    Techniques Used: Biomarker Discovery, Labeling

    Related Articles

    Biomarker Discovery:

    Article Title: The role of machine learning in clinical research: transforming the future of evidence generation
    Article Snippet: For instance, IQVIA Inc. has described the ability to automatically process some adverse events related to drug therapies using a combination of optical character recognition and NLP, though this technique has not been described in peer-reviewed publications [ ].

    Labeling:

    Article Title: The role of machine learning in clinical research: transforming the future of evidence generation
    Article Snippet: For instance, IQVIA Inc. has described the ability to automatically process some adverse events related to drug therapies using a combination of optical character recognition and NLP, though this technique has not been described in peer-reviewed publications [ ].



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    built in optical character recognition function  (MathWorks Inc)
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    Example output of fiducial and label detection. The blue color component is shown, with boxed connected components in yellow. The fiducial locations are indicated as cyan and magenta asterisks. Letter labels, as output from the letter <t>recognition</t> function, are shown above the image patch in magenta. Here, all labels are correct except for “N”, which is mislabeled as “M”.
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    Image Search Results


    Example output of fiducial and label detection. The blue color component is shown, with boxed connected components in yellow. The fiducial locations are indicated as cyan and magenta asterisks. Letter labels, as output from the letter recognition function, are shown above the image patch in magenta. Here, all labels are correct except for “N”, which is mislabeled as “M”.

    Journal: Proceedings of SPIE--the International Society for Optical Engineering

    Article Title: Textual fiducial detection in breast conserving surgery for a near-real time image guidance system

    doi: 10.1117/12.2550662

    Figure Lengend Snippet: Example output of fiducial and label detection. The blue color component is shown, with boxed connected components in yellow. The fiducial locations are indicated as cyan and magenta asterisks. Letter labels, as output from the letter recognition function, are shown above the image patch in magenta. Here, all labels are correct except for “N”, which is mislabeled as “M”.

    Article Snippet: Letters can be recognized with 89% accuracy using the MATLAB built in optical character recognition function, and an average of 81% of points can be accurately labeled and localized.

    Techniques:

    Examples of blue color component image patches that were fed into the optical character recognition function and mislabeled. The output label is displayed in magenta. The fiducial location, as determined by the brightest pixel in the red color channel (not shown), is indicated with a cyan and magenta asterisk.

    Journal: Proceedings of SPIE--the International Society for Optical Engineering

    Article Title: Textual fiducial detection in breast conserving surgery for a near-real time image guidance system

    doi: 10.1117/12.2550662

    Figure Lengend Snippet: Examples of blue color component image patches that were fed into the optical character recognition function and mislabeled. The output label is displayed in magenta. The fiducial location, as determined by the brightest pixel in the red color channel (not shown), is indicated with a cyan and magenta asterisk.

    Article Snippet: Letters can be recognized with 89% accuracy using the MATLAB built in optical character recognition function, and an average of 81% of points can be accurately labeled and localized.

    Techniques: