Identification of tophi in ultrasound imaging based on transfer learning and clinical practice

• Published in: Scientific reports Vol. 13; no. 1; pp. 12507 - 7
• Main Authors: Lin, Tzu-Min, Lee, Hsiang-Yen, Chang, Ching-Kuei, Lin, Ke-Hung, Chang, Chi-Ching, Wu, Bing-Fei, Peng, Syu-Jyun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.08.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/250; 692/4023; Arthritis; Arthritis, Gouty; Computed tomography; Crystals; Gout; Gout - diagnostic imaging; Humanities and Social Sciences; Humans; Inflammation; Machine Learning; Metabolic disorders; multidisciplinary; Neural networks; Reproducibility of Results; Rheumatism; Science; Science (multidisciplinary); Tomography; Tomography, X-Ray Computed - methods; Transfer learning; Ultrasonic imaging; Ultrasonography - methods; Ultrasound; Uric acid; Uric Acid - metabolism
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-39508-5

Gout is a common metabolic disorder characterized by deposits of monosodium urate monohydrate crystals (tophi) in soft tissue, triggering intense and acute arthritis with intolerable pain as well as articular and periarticular inflammation. Tophi can also promote chronic inflammatory and erosive arthritis. 2015 ACR/EULAR Gout Classification criteria include clinical, laboratory, and imaging findings, where cases of gout are indicated by a threshold score of ≥ 8. Some imaging-related findings, such as a double contour sign in ultrasound, urate in dual-energy computed tomography, or radiographic gout-related erosion, generate a score of up to 4. Clearly, the diagnosis of gout is largely assisted by imaging findings; however, dual-energy computed tomography is expensive and exposes the patient to high levels of radiation. Although musculoskeletal ultrasound is non-invasive and inexpensive, the reliability of the results depends on expert experience. In the current study, we applied transfer learning to train a convolutional neural network for the identification of tophi in ultrasound images. The accuracy of predictions varied with the convolutional neural network model, as follows: InceptionV3 (0.871 ± 0.020), ResNet101 (0.913 ± 0.015), and VGG19 (0.918 ± 0.020). The sensitivity was as follows: InceptionV3 (0.507 ± 0.060), ResNet101 (0.680 ± 0.056), and VGG19 (0.747 ± 0.056). The precision was as follows: InceptionV3 (0.767 ± 0.091), ResNet101 (0.863 ± 0.098), and VGG19 (0.825 ± 0.062). Our results demonstrate that it is possible to retrain deep convolutional neural networks to identify the patterns of tophi in ultrasound images with a high degree of accuracy.