Can AI Automatically Assess Scan Quality of Hip Ultrasound?
Ultrasound images can reliably detect Developmental Dysplasia of the Hip (DDH) during early infancy. Accuracy of diagnosis depends on the scan quality, which is subjectively assessed by the sonographer during ultrasound examination. Such assessment is prone to errors and often results in poor-qualit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/8/4072 |
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| author | Abhilash Rakkunedeth Hareendrananthan Myles Mabee Baljot S. Chahal Sukhdeep K. Dulai Jacob L. Jaremko |
| author_facet | Abhilash Rakkunedeth Hareendrananthan Myles Mabee Baljot S. Chahal Sukhdeep K. Dulai Jacob L. Jaremko |
| author_sort | Abhilash Rakkunedeth Hareendrananthan |
| collection | DOAJ |
| description | Ultrasound images can reliably detect Developmental Dysplasia of the Hip (DDH) during early infancy. Accuracy of diagnosis depends on the scan quality, which is subjectively assessed by the sonographer during ultrasound examination. Such assessment is prone to errors and often results in poor-quality scans not being reported, risking misdiagnosis. In this paper, we propose an Artificial Intelligence (AI) technique for automatically determining scan quality. We trained a Convolutional Neural Network (CNN) to categorize 3D Ultrasound (3DUS) hip scans as ‘adequate’ or ‘inadequate’ for diagnosis. We evaluated the performance of this AI technique on two datasets—Dataset 1 (DS1) consisting of 2187 3DUS images in which each image was assessed by one reader for scan quality on a scale of 1 (lowest quality) to 5 (optimal quality) and Dataset 2 (DS2) consisting of 107 3DUS images evaluated semi-quantitatively by four readers using a 10-point scoring system. As a binary classifier (adequate/inadequate), the AI technique gave highly accurate predictions on both datasets (DS1 accuracy = 96% and DS2 accuracy = 91%) and showed high agreement with expert readings in terms of Intraclass Correlation Coefficient (ICC) and Cohen’s kappa coefficient (K). Using our AI-based approach as a screening tool during ultrasound scanning or postprocessing would ensure high scan quality and lead to more reliable ultrasound hip examination in infants. |
| first_indexed | 2024-03-09T11:11:26Z |
| format | Article |
| id | doaj.art-cc5de2b0e3fd4fa18711c127b615207b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T11:11:26Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-cc5de2b0e3fd4fa18711c127b615207b2023-12-01T00:44:45ZengMDPI AGApplied Sciences2076-34172022-04-01128407210.3390/app12084072Can AI Automatically Assess Scan Quality of Hip Ultrasound?Abhilash Rakkunedeth Hareendrananthan0Myles Mabee1Baljot S. Chahal2Sukhdeep K. Dulai3Jacob L. Jaremko4Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB T6G 2B7, CanadaCollege of Medicine, University of Saskatchewan, 107 Wiggins Rd, Saskatoon, SK S7N 5E5, CanadaDepartment of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB T6G 2B7, CanadaDepartment of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB T6G 2B7, CanadaDepartment of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB T6G 2B7, CanadaUltrasound images can reliably detect Developmental Dysplasia of the Hip (DDH) during early infancy. Accuracy of diagnosis depends on the scan quality, which is subjectively assessed by the sonographer during ultrasound examination. Such assessment is prone to errors and often results in poor-quality scans not being reported, risking misdiagnosis. In this paper, we propose an Artificial Intelligence (AI) technique for automatically determining scan quality. We trained a Convolutional Neural Network (CNN) to categorize 3D Ultrasound (3DUS) hip scans as ‘adequate’ or ‘inadequate’ for diagnosis. We evaluated the performance of this AI technique on two datasets—Dataset 1 (DS1) consisting of 2187 3DUS images in which each image was assessed by one reader for scan quality on a scale of 1 (lowest quality) to 5 (optimal quality) and Dataset 2 (DS2) consisting of 107 3DUS images evaluated semi-quantitatively by four readers using a 10-point scoring system. As a binary classifier (adequate/inadequate), the AI technique gave highly accurate predictions on both datasets (DS1 accuracy = 96% and DS2 accuracy = 91%) and showed high agreement with expert readings in terms of Intraclass Correlation Coefficient (ICC) and Cohen’s kappa coefficient (K). Using our AI-based approach as a screening tool during ultrasound scanning or postprocessing would ensure high scan quality and lead to more reliable ultrasound hip examination in infants.https://www.mdpi.com/2076-3417/12/8/4072hipdevelopmental dysplasia of the hip3D ultrasoundscan quality assessmentdeep learningconvolutional neural networks |
| spellingShingle | Abhilash Rakkunedeth Hareendrananthan Myles Mabee Baljot S. Chahal Sukhdeep K. Dulai Jacob L. Jaremko Can AI Automatically Assess Scan Quality of Hip Ultrasound? Applied Sciences hip developmental dysplasia of the hip 3D ultrasound scan quality assessment deep learning convolutional neural networks |
| title | Can AI Automatically Assess Scan Quality of Hip Ultrasound? |
| title_full | Can AI Automatically Assess Scan Quality of Hip Ultrasound? |
| title_fullStr | Can AI Automatically Assess Scan Quality of Hip Ultrasound? |
| title_full_unstemmed | Can AI Automatically Assess Scan Quality of Hip Ultrasound? |
| title_short | Can AI Automatically Assess Scan Quality of Hip Ultrasound? |
| title_sort | can ai automatically assess scan quality of hip ultrasound |
| topic | hip developmental dysplasia of the hip 3D ultrasound scan quality assessment deep learning convolutional neural networks |
| url | https://www.mdpi.com/2076-3417/12/8/4072 |
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