Liver fibrosis stage classification in stacked microvascular images based on deep learning
Abstract Background Monitoring fibrosis in patients with chronic liver disease (CLD) is an important management strategy. We have already reported a novel stacked microvascular imaging (SMVI) technique and an examiner scoring evaluation method to improve fibrosis assessment accuracy and demonstrate...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-01-01
|
| Series: | BMC Medical Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12880-024-01531-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841544210593873920 |
|---|---|
| author | Daisuke Miura Hiromi Suenaga Rino Hiwatashi Shingo Mabu |
| author_facet | Daisuke Miura Hiromi Suenaga Rino Hiwatashi Shingo Mabu |
| author_sort | Daisuke Miura |
| collection | DOAJ |
| description | Abstract Background Monitoring fibrosis in patients with chronic liver disease (CLD) is an important management strategy. We have already reported a novel stacked microvascular imaging (SMVI) technique and an examiner scoring evaluation method to improve fibrosis assessment accuracy and demonstrate its high sensitivity. In the present study, we analyzed the effectiveness and objectivity of SMVI in diagnosing the liver fibrosis stage based on artificial intelligence (AI). Methods This single-center, cross-sectional study included 517 patients with CLD who underwent ultrasonography and liver stiffness testing between August 2019 and October 2022. A convolutional neural network model was constructed to evaluate the degree of liver fibrosis from stacked microvascular images generated by accumulating high-sensitivity Doppler (i.e., high-definition color) images from these patients. In contrast, as a method of judgment by the human eye, we focused on three hallmarks of intrahepatic microvessel morphological changes in the stacked microvascular images: narrowing, caliber irregularity, and tortuosity. The degree of liver fibrosis was classified into five stages according to etiology based on liver stiffness measurement: F0–1Low (< 5.0 kPa), F0–1High (≥ 5.0 kPa), F2, F3, and F4. Results The AI classification accuracy was 53.8% for a 5-class classification, 66.3% for a 3-class classification (F0–1Low vs. F0–1High vs. F2–4), and 83.8% for a 2-class classification (F0–1 vs. F2–4). The diagnostic accuracy for ≥ F2 was 81.6% in the examiner’s score assessment, compared with 83.8% in AI assessment, indicating that AI achieved higher diagnostic accuracy. Similarly, AI demonstrated higher sensitivity and specificity of 84.2% and 83.5%, respectively. Comparing human judgement with AI judgement, the AI analysis was a superior model with a higher F1 score in the 2-class classification. Conclusions In detecting significant fibrosis (≥ F2) using the SMVI method, AI-based assessments are more accurate than human judgement; moreover, AI-based SMVI analysis eliminating human subjectivity bias and determining patients with objective fibrosis development is considered an important improvement. |
| format | Article |
| id | doaj-art-b319bbc5513549d586a46bf37ea320fd |
| institution | Kabale University |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-b319bbc5513549d586a46bf37ea320fd2025-01-12T12:44:48ZengBMCBMC Medical Imaging1471-23422025-01-0125111110.1186/s12880-024-01531-xLiver fibrosis stage classification in stacked microvascular images based on deep learningDaisuke Miura0Hiromi Suenaga1Rino Hiwatashi2Shingo Mabu3Department of Ultrasound and Clinical Laboratory, Fukuoka Tokushukai HospitalDepartment of Laboratory Science, Yamaguchi University Graduate School of MedicineDepartment of Ultrasound and Clinical Laboratory, Fukuoka Tokushukai HospitalDepartment of Information Science and Engineering, Graduate School of Sciences and Technology for Innovation, Yamaguchi UniversityAbstract Background Monitoring fibrosis in patients with chronic liver disease (CLD) is an important management strategy. We have already reported a novel stacked microvascular imaging (SMVI) technique and an examiner scoring evaluation method to improve fibrosis assessment accuracy and demonstrate its high sensitivity. In the present study, we analyzed the effectiveness and objectivity of SMVI in diagnosing the liver fibrosis stage based on artificial intelligence (AI). Methods This single-center, cross-sectional study included 517 patients with CLD who underwent ultrasonography and liver stiffness testing between August 2019 and October 2022. A convolutional neural network model was constructed to evaluate the degree of liver fibrosis from stacked microvascular images generated by accumulating high-sensitivity Doppler (i.e., high-definition color) images from these patients. In contrast, as a method of judgment by the human eye, we focused on three hallmarks of intrahepatic microvessel morphological changes in the stacked microvascular images: narrowing, caliber irregularity, and tortuosity. The degree of liver fibrosis was classified into five stages according to etiology based on liver stiffness measurement: F0–1Low (< 5.0 kPa), F0–1High (≥ 5.0 kPa), F2, F3, and F4. Results The AI classification accuracy was 53.8% for a 5-class classification, 66.3% for a 3-class classification (F0–1Low vs. F0–1High vs. F2–4), and 83.8% for a 2-class classification (F0–1 vs. F2–4). The diagnostic accuracy for ≥ F2 was 81.6% in the examiner’s score assessment, compared with 83.8% in AI assessment, indicating that AI achieved higher diagnostic accuracy. Similarly, AI demonstrated higher sensitivity and specificity of 84.2% and 83.5%, respectively. Comparing human judgement with AI judgement, the AI analysis was a superior model with a higher F1 score in the 2-class classification. Conclusions In detecting significant fibrosis (≥ F2) using the SMVI method, AI-based assessments are more accurate than human judgement; moreover, AI-based SMVI analysis eliminating human subjectivity bias and determining patients with objective fibrosis development is considered an important improvement.https://doi.org/10.1186/s12880-024-01531-xArtificial intelligenceDeep learningLiver cirrhosisMicrovascular imagingStacked microvascular imaging |
| spellingShingle | Daisuke Miura Hiromi Suenaga Rino Hiwatashi Shingo Mabu Liver fibrosis stage classification in stacked microvascular images based on deep learning BMC Medical Imaging Artificial intelligence Deep learning Liver cirrhosis Microvascular imaging Stacked microvascular imaging |
| title | Liver fibrosis stage classification in stacked microvascular images based on deep learning |
| title_full | Liver fibrosis stage classification in stacked microvascular images based on deep learning |
| title_fullStr | Liver fibrosis stage classification in stacked microvascular images based on deep learning |
| title_full_unstemmed | Liver fibrosis stage classification in stacked microvascular images based on deep learning |
| title_short | Liver fibrosis stage classification in stacked microvascular images based on deep learning |
| title_sort | liver fibrosis stage classification in stacked microvascular images based on deep learning |
| topic | Artificial intelligence Deep learning Liver cirrhosis Microvascular imaging Stacked microvascular imaging |
| url | https://doi.org/10.1186/s12880-024-01531-x |
| work_keys_str_mv | AT daisukemiura liverfibrosisstageclassificationinstackedmicrovascularimagesbasedondeeplearning AT hiromisuenaga liverfibrosisstageclassificationinstackedmicrovascularimagesbasedondeeplearning AT rinohiwatashi liverfibrosisstageclassificationinstackedmicrovascularimagesbasedondeeplearning AT shingomabu liverfibrosisstageclassificationinstackedmicrovascularimagesbasedondeeplearning |