A Deep-Learning Approach to Spleen Volume Estimation in Patients with Gaucher Disease
The enlargement of the liver and spleen (hepatosplenomegaly) is a common manifestation of Gaucher disease (GD). An accurate estimation of the liver and spleen volumes in patients with GD, using imaging tools such as magnetic resonance imaging (MRI), is crucial for the baseline assessment and monitor...
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
|
| Series: | Journal of Clinical Medicine |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0383/12/16/5361 |
| _version_ | 1797435932997058560 |
|---|---|
| author | Ido Azuri Ameer Wattad Keren Peri-Hanania Tamar Kashti Ronnie Rosen Yaron Caspi Majdolen Istaiti Makram Wattad Yaakov Applbaum Ari Zimran Shoshana Revel-Vilk Yonina C. Eldar |
| author_facet | Ido Azuri Ameer Wattad Keren Peri-Hanania Tamar Kashti Ronnie Rosen Yaron Caspi Majdolen Istaiti Makram Wattad Yaakov Applbaum Ari Zimran Shoshana Revel-Vilk Yonina C. Eldar |
| author_sort | Ido Azuri |
| collection | DOAJ |
| description | The enlargement of the liver and spleen (hepatosplenomegaly) is a common manifestation of Gaucher disease (GD). An accurate estimation of the liver and spleen volumes in patients with GD, using imaging tools such as magnetic resonance imaging (MRI), is crucial for the baseline assessment and monitoring of the response to treatment. A commonly used method in clinical practice to estimate the spleen volume is the employment of a formula that uses the measurements of the craniocaudal length, diameter, and thickness of the spleen in MRI. However, the inaccuracy of this formula is significant, which, in turn, emphasizes the need for a more precise and reliable alternative. To this end, we employed deep-learning techniques, to achieve a more accurate spleen segmentation and, subsequently, calculate the resulting spleen volume with higher accuracy on a testing set cohort of 20 patients with GD. Our results indicate that the mean error obtained using the deep-learning approach to spleen volume estimation is 3.6 ± 2.7%, which is significantly lower than the common formula approach, which resulted in a mean error of 13.9 ± 9.6%. These findings suggest that the integration of deep-learning methods into the clinical routine practice for spleen volume calculation could lead to improved diagnostic and monitoring outcomes. |
| first_indexed | 2024-03-09T10:55:25Z |
| format | Article |
| id | doaj.art-84b84090ed554bddbd8eb05c80b5e7ed |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-09T10:55:25Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-84b84090ed554bddbd8eb05c80b5e7ed2023-12-01T01:43:34ZengMDPI AGJournal of Clinical Medicine2077-03832023-08-011216536110.3390/jcm12165361A Deep-Learning Approach to Spleen Volume Estimation in Patients with Gaucher DiseaseIdo Azuri0Ameer Wattad1Keren Peri-Hanania2Tamar Kashti3Ronnie Rosen4Yaron Caspi5Majdolen Istaiti6Makram Wattad7Yaakov Applbaum8Ari Zimran9Shoshana Revel-Vilk10Yonina C. Eldar11Bioinformatics Unit, Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot 7610001, IsraelDepartment of Radiology, Shaare Zedek Medical Center, Jerusalem 9103102, IsraelDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, IsraelDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, IsraelDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, IsraelDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, IsraelGaucher Unit, Shaare Zedek Medical Center, Jerusalem 9103102, IsraelDepartment of Radiology, Shaare Zedek Medical Center, Jerusalem 9103102, IsraelDepartment of Radiology, Shaare Zedek Medical Center, Jerusalem 9103102, IsraelGaucher Unit, Shaare Zedek Medical Center, Jerusalem 9103102, IsraelGaucher Unit, Shaare Zedek Medical Center, Jerusalem 9103102, IsraelDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, IsraelThe enlargement of the liver and spleen (hepatosplenomegaly) is a common manifestation of Gaucher disease (GD). An accurate estimation of the liver and spleen volumes in patients with GD, using imaging tools such as magnetic resonance imaging (MRI), is crucial for the baseline assessment and monitoring of the response to treatment. A commonly used method in clinical practice to estimate the spleen volume is the employment of a formula that uses the measurements of the craniocaudal length, diameter, and thickness of the spleen in MRI. However, the inaccuracy of this formula is significant, which, in turn, emphasizes the need for a more precise and reliable alternative. To this end, we employed deep-learning techniques, to achieve a more accurate spleen segmentation and, subsequently, calculate the resulting spleen volume with higher accuracy on a testing set cohort of 20 patients with GD. Our results indicate that the mean error obtained using the deep-learning approach to spleen volume estimation is 3.6 ± 2.7%, which is significantly lower than the common formula approach, which resulted in a mean error of 13.9 ± 9.6%. These findings suggest that the integration of deep-learning methods into the clinical routine practice for spleen volume calculation could lead to improved diagnostic and monitoring outcomes.https://www.mdpi.com/2077-0383/12/16/5361Gaucher diseasespleen volumedeep learning |
| spellingShingle | Ido Azuri Ameer Wattad Keren Peri-Hanania Tamar Kashti Ronnie Rosen Yaron Caspi Majdolen Istaiti Makram Wattad Yaakov Applbaum Ari Zimran Shoshana Revel-Vilk Yonina C. Eldar A Deep-Learning Approach to Spleen Volume Estimation in Patients with Gaucher Disease Journal of Clinical Medicine Gaucher disease spleen volume deep learning |
| title | A Deep-Learning Approach to Spleen Volume Estimation in Patients with Gaucher Disease |
| title_full | A Deep-Learning Approach to Spleen Volume Estimation in Patients with Gaucher Disease |
| title_fullStr | A Deep-Learning Approach to Spleen Volume Estimation in Patients with Gaucher Disease |
| title_full_unstemmed | A Deep-Learning Approach to Spleen Volume Estimation in Patients with Gaucher Disease |
| title_short | A Deep-Learning Approach to Spleen Volume Estimation in Patients with Gaucher Disease |
| title_sort | deep learning approach to spleen volume estimation in patients with gaucher disease |
| topic | Gaucher disease spleen volume deep learning |
| url | https://www.mdpi.com/2077-0383/12/16/5361 |
| work_keys_str_mv | AT idoazuri adeeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT ameerwattad adeeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT kerenperihanania adeeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT tamarkashti adeeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT ronnierosen adeeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT yaroncaspi adeeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT majdolenistaiti adeeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT makramwattad adeeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT yaakovapplbaum adeeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT arizimran adeeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT shoshanarevelvilk adeeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT yoninaceldar adeeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT idoazuri deeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT ameerwattad deeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT kerenperihanania deeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT tamarkashti deeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT ronnierosen deeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT yaroncaspi deeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT majdolenistaiti deeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT makramwattad deeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT yaakovapplbaum deeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT arizimran deeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT shoshanarevelvilk deeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease AT yoninaceldar deeplearningapproachtospleenvolumeestimationinpatientswithgaucherdisease |