Automated quantification of penile curvature using artificial intelligence
ObjectiveTo develop and validate an artificial intelligence (AI)-based algorithm for capturing automated measurements of Penile curvature (PC) based on 2-dimensional images.Materials and methodsNine 3D-printed penile models with differing curvature angles (ranging from 18 to 88°) were used to compil...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2022.954497/full |
| _version_ | 1828148074956980224 |
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| author | Tariq O. Abbas Tariq O. Abbas Tariq O. Abbas Mohamed AbdelMoniem Muhammad E. H. Chowdhury |
| author_facet | Tariq O. Abbas Tariq O. Abbas Tariq O. Abbas Mohamed AbdelMoniem Muhammad E. H. Chowdhury |
| author_sort | Tariq O. Abbas |
| collection | DOAJ |
| description | ObjectiveTo develop and validate an artificial intelligence (AI)-based algorithm for capturing automated measurements of Penile curvature (PC) based on 2-dimensional images.Materials and methodsNine 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.ResultsThe 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.ConclusionsConsidering 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. |
| first_indexed | 2024-04-11T21:09:34Z |
| format | Article |
| id | doaj.art-50adb2b94f6c43f5b72fcfa935ef4de4 |
| institution | Directory Open Access Journal |
| issn | 2624-8212 |
| language | English |
| last_indexed | 2024-04-11T21:09:34Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj.art-50adb2b94f6c43f5b72fcfa935ef4de42022-12-22T04:03:03ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122022-08-01510.3389/frai.2022.954497954497Automated quantification of penile curvature using artificial intelligenceTariq O. Abbas0Tariq O. Abbas1Tariq O. Abbas2Mohamed AbdelMoniem3Muhammad E. H. Chowdhury4Weill Cornell Medicine-Qatar, Ar-Rayyan, QatarUrology Division, Surgery Department, Sidra Medicine, Doha, QatarCollege of Medicine, Qatar University, Doha, QatarCollege of Medicine, Qatar University, Doha, QatarDepartment of Electrical Engineering, Qatar University, Doha, QatarObjectiveTo develop and validate an artificial intelligence (AI)-based algorithm for capturing automated measurements of Penile curvature (PC) based on 2-dimensional images.Materials and methodsNine 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.ResultsThe 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.ConclusionsConsidering 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.https://www.frontiersin.org/articles/10.3389/frai.2022.954497/fullpenile curvatureartificial intelligencemachine learninghypospadiaschordee |
| spellingShingle | Tariq O. Abbas Tariq O. Abbas Tariq O. Abbas Mohamed AbdelMoniem Muhammad E. H. Chowdhury Automated quantification of penile curvature using artificial intelligence Frontiers in Artificial Intelligence penile curvature artificial intelligence machine learning hypospadias chordee |
| 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 | penile curvature artificial intelligence machine learning hypospadias chordee |
| url | https://www.frontiersin.org/articles/10.3389/frai.2022.954497/full |
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