A New Automated AI-Assisted System to Assess Cervical Disc Herniation. Issue 16 (15th August 2022)

• Record Type: Journal Article
• Authors: Fu, Su; Zhang, Chunlin; Yan, Xu; Li, Dongzhe; Wang, Yongkui; Dong, Chao; Cao, Zhengming; Ning, Yongming; Shao, Chenglong; Yang, Tengyue
• Is Part Of: Spine. Volume 47:Issue 16(2022)
• Page Start: E536
• Page End: E544
• Publication Date: 2022-08-15
• Subjects: cervical disc herniation -- volumetric measurement -- medical software; Spine -- Abnormalities -- Periodicals; Spine -- Diseases -- Periodicals; Spine -- Surgery -- Periodicals; 616.73005
• DOI: 10.1097/BRS.0000000000004376
• Languages: English
• ISSNs: 0362-2436

Abstract

Abstract : Study Design: An algorithm was developed with MATLAB platform to automatically quantify the volume of cervical disc herniation (CDH) based on the sagittal magnetic resonance images. This automated program was used for CDH data set, and then compared with manual measurement results confirming its reliability. Objective: The aim was to develop a new software for automated CDH volume measurement. Summary of Background Data: CDH compresses the spinal cord, regarding as the leading cause of cervical myelopathy. However, the CDH volume, of great value to clinical symptoms, can be only manually measured with no-excellent but acceptable interobserver reliability. This was due to the manual error of outlining CDH area and inclusion of structure posterior vertebra. No studies has proposed such an automated algorithm of CDH volume quantification which is standardised to quantify the accurate volume of CDH thus helping doctors easily evaluate CDH progressing. Methods: The algorithm of CDH volume measurement was proposed. This program was then tested for 490 CDHs data set, from 185 patients with two repeated magnetic resonance imaging detections. Three individual observers manually measured the volumes of these CDHs, to justify the accuracy of this software. CDH volume was either in the classic way or the revised way excluding the influence of structure posterior vertebra. Results: The automated software was successfully developed on MATLAB platform, with no difference found with manual measurements (average level) in CDH volume measurement. The change ratios in CDH volumes were profoundly consistent with manual observation, showing the error of 5.8% in median. The revised method elevated the absolute value of ratio by amplifying the percentage change. Conclusion: Our developed automated volumetry system was an standardized and accurate way, with selective removal module of structure posterior vertebra, replaceable for manual volume measurement of CDH, which was useful for spinal surgeons diagnosing and treating CDH disease.