Hybrid Deep Learning and Machine Learning for Detecting Hepatocyte Ballooning in Liver Ultrasound Images
These results indicate that this combination method can decrease the need for invasive liver biopsies by providing a non-invasive and precise alternative for early identification and monitoring of NAFLD and NASH. Subsequent research will prioritize the validation of these models using larger datasets from multiple centers to evaluate their generalizability and incorporation into clinical practice.
Authors
- Abdulaziz Alosaimi
- Jens Schneider (ORCID: https://orcid.org/0000-0003-2680-9628)
- Fahad Muflih Alshagathrh (ORCID: https://orcid.org/0000-0001-7797-0417)
- Mowafa Househ
- Bushra Alahmadi (ORCID: https://orcid.org/0000-0003-1917-4871)
- Mahmood Alzubaidi (ORCID: https://orcid.org/0000-0002-6082-7873)
- Ali Aldhebaib
- Metab Alkubeyyer (ORCID: https://orcid.org/0000-0002-0627-8551)
- Khalid Alswat (ORCID: https://orcid.org/0000-0002-3521-8523)
- Samuel Gecík
- Maram Alkhamash
- Amani Alsadoon
Institutions
- Technical University of Košice (SK)
- King Saud bin Abdulaziz University for Health Sciences (SA)
- King Saud Medical City (SA)
- King Saud University (SA)
- King Abdulaziz Medical City (SA)
- Hamad bin Khalifa University (QA)
Publication Details
- Journal
- Diagnostics
- Published
- 2024-11-24
- DOI
- https://doi.org/10.3390/diagnostics14232646
- Citations
- 6
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.32
Funders
- King Saud University
- College of Medicine, King Saud University