Automated quantification of penile curvature using artificial intelligence
<h3>Objective</h3><p dir="ltr">To develop and validate an artificial intelligence (AI)-based algorithm for capturing automated measurements of Penile curvature (PC) based on 2-dimensional images.</p><h3>Materials and methods</h3><p dir="ltr"...
محفوظ في:
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| مؤلفون آخرون: | , |
| منشور في: |
2022
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إضافة وسم
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| _version_ | 1864513547876171776 |
|---|---|
| author | Tariq O. Abbas (11247771) |
| author2 | Mohamed AbdelMoniem (21385457) Muhammad E. H. Chowdhury (14150526) |
| author2_role | author author |
| author_facet | Tariq O. Abbas (11247771) Mohamed AbdelMoniem (21385457) Muhammad E. H. Chowdhury (14150526) |
| author_role | author |
| dc.creator.none.fl_str_mv | Tariq O. Abbas (11247771) Mohamed AbdelMoniem (21385457) Muhammad E. H. Chowdhury (14150526) |
| dc.date.none.fl_str_mv | 2022-08-30T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3389/frai.2022.954497 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Automated_quantification_of_penile_curvature_using_artificial_intelligence/29098430 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering penile curvature artificial intelligence machine learning hypospadias chordee |
| dc.title.none.fl_str_mv | Automated quantification of penile curvature using artificial intelligence |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Objective</h3><p dir="ltr">To develop and validate an artificial intelligence (AI)-based algorithm for capturing automated measurements of Penile curvature (PC) based on 2-dimensional images.</p><h3>Materials and methods</h3><p dir="ltr">Nine 3D-printed penile models with differing curvature angles (ranging from 18 to 88°) were used to compile a 900-image dataset featuring multiple camera positions, inclination angles, and background/lighting conditions. The proposed framework of PC angle estimation consisted of three stages: automatic penile area localization, shaft segmentation, and curvature angle estimation. The penile model images were captured using a smartphone camera and used to train and test a Yolov5 model that automatically cropped the penile area from each image. Next, an Unet-based segmentation model was trained, validated, and tested to segment the penile shaft, before a custom Hough-Transform-based angle estimation technique was used to evaluate degree of PC.</p><h3>Results</h3><p dir="ltr">The proposed framework displayed robust performance in cropping the penile area [mean average precision (mAP) 99.4%] and segmenting the shaft [Dice Similarity Coefficient (DSC) 98.4%]. Curvature angle estimation technique generally demonstrated excellent performance, with a mean absolute error (MAE) of just 8.5 when compared with ground truth curvature angles.</p><h3>Conclusions</h3><p dir="ltr">Considering current intra- and inter-surgeon variability of PC assessments, the framework reported here could significantly improve precision of PC measurements by surgeons and hypospadiology researchers.</p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Artificial Intelligence<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/frai.2022.954497" target="_blank">https://dx.doi.org/10.3389/frai.2022.954497</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_f72eccea478c46b5e19f1e14d6c56833 |
| identifier_str_mv | 10.3389/frai.2022.954497 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29098430 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Automated quantification of penile curvature using artificial intelligenceTariq O. Abbas (11247771)Mohamed AbdelMoniem (21385457)Muhammad E. H. Chowdhury (14150526)Biomedical and clinical sciencesClinical sciencesEngineeringBiomedical engineeringpenile curvatureartificial intelligencemachine learninghypospadiaschordee<h3>Objective</h3><p dir="ltr">To develop and validate an artificial intelligence (AI)-based algorithm for capturing automated measurements of Penile curvature (PC) based on 2-dimensional images.</p><h3>Materials and methods</h3><p dir="ltr">Nine 3D-printed penile models with differing curvature angles (ranging from 18 to 88°) were used to compile a 900-image dataset featuring multiple camera positions, inclination angles, and background/lighting conditions. The proposed framework of PC angle estimation consisted of three stages: automatic penile area localization, shaft segmentation, and curvature angle estimation. The penile model images were captured using a smartphone camera and used to train and test a Yolov5 model that automatically cropped the penile area from each image. Next, an Unet-based segmentation model was trained, validated, and tested to segment the penile shaft, before a custom Hough-Transform-based angle estimation technique was used to evaluate degree of PC.</p><h3>Results</h3><p dir="ltr">The proposed framework displayed robust performance in cropping the penile area [mean average precision (mAP) 99.4%] and segmenting the shaft [Dice Similarity Coefficient (DSC) 98.4%]. Curvature angle estimation technique generally demonstrated excellent performance, with a mean absolute error (MAE) of just 8.5 when compared with ground truth curvature angles.</p><h3>Conclusions</h3><p dir="ltr">Considering current intra- and inter-surgeon variability of PC assessments, the framework reported here could significantly improve precision of PC measurements by surgeons and hypospadiology researchers.</p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Artificial Intelligence<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/frai.2022.954497" target="_blank">https://dx.doi.org/10.3389/frai.2022.954497</a></p>2022-08-30T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3389/frai.2022.954497https://figshare.com/articles/journal_contribution/Automated_quantification_of_penile_curvature_using_artificial_intelligence/29098430CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290984302022-08-30T09:00:00Z |
| spellingShingle | Automated quantification of penile curvature using artificial intelligence Tariq O. Abbas (11247771) Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering penile curvature artificial intelligence machine learning hypospadias chordee |
| status_str | publishedVersion |
| title | Automated quantification of penile curvature using artificial intelligence |
| title_full | Automated quantification of penile curvature using artificial intelligence |
| title_fullStr | Automated quantification of penile curvature using artificial intelligence |
| title_full_unstemmed | Automated quantification of penile curvature using artificial intelligence |
| title_short | Automated quantification of penile curvature using artificial intelligence |
| title_sort | Automated quantification of penile curvature using artificial intelligence |
| topic | Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering penile curvature artificial intelligence machine learning hypospadias chordee |