An intelligent point-of-care photoacoustic and ultrasound dual-modality imaging system for Rheumatoid arthritis in fingers: Technical development and initial clinical test.
Authors
Affiliations (8)
Affiliations (8)
- Department of Biomedical Engineering, University of Michigan, 1150 Medical Center Dr, Ann Arbor, MI, 48109, USA.
- Department of Robotics, University of Michigan, 2505 Hayward St, Ann Arbor, MI, 48109, USA.
- GE HealthCare Technology & Innovation Center, Niskayuna, NY, 12309, USA.
- Division of Rheumatology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI, 48105, USA.
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 48104, USA.
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA.
Abstract
Aiming at an intelligent point-of-care imaging technology for rheumatology clinics, a fully automatic 3D photoacoustic (PA) and ultrasound (US) dual-modality system driven by a robot and powered by deep learning (DL)-based image processing was developed. Automated scanning of volumetric images from patient joints, plus DL-based tissue segmentation and quantification of imaging biomarkers, ensures that the measurements from this system are objective and reproducible. Clinical validation was conducted via a longitudinal study on 43 finger joints from patients affected by inflammatory arthritis. Using manual segmentations as the gold standard, our DL algorithm utilizing the 3D Deep Attentive Feature (DAF3D) model showed satisfactory performance in automatic segmentation of joint space and synovial region and achieved a Dice score of 0.77±0.03 and an IoU of 0.64±0.03. Based on the tissue segmentations facilitated by the DAF3D model, six volumetric imaging biomarkers reflecting the activity of arthritis and its change in response to the treatment were quantified, including hyperemia, blood oxygenation, US power Doppler, joint space echogenicity, joint space volume, and synovial volume. The imaging biomarkers quantified from DL-based segmentation and manual segmentation showed moderate to strong correlations ( <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>R</mi></math> : 0.41-0.86). Hyperemia quantified from PA imaging has the strongest association with the disease activity indicated by Clinical Assessment Questionnaire (CAQ) scores, with <math xmlns="http://www.w3.org/1998/Math/MathML"> <msup><mrow><mi>R</mi></mrow> <mrow><mn>2</mn></mrow> </msup> </math> =0.41. Linear models combining the two biomarkers from PA imaging, the four biomarkers from US imaging, and all six imaging biomarkers rendered moderate to very strong associations with the disease activity scores, with <math xmlns="http://www.w3.org/1998/Math/MathML"> <msup><mrow><mi>R</mi></mrow> <mrow><mn>2</mn></mrow> </msup> </math> of 0.41, 0.26, and 0.52, respectively.