Abstract
People with hemophilia require frequent diagnoses of joint bleeding. This is currently achieved with visits to specialized centers. One possibility is to have a point-of-care acquisition of the ultrasound joint image by the patients themselves, followed by a remote evaluation by the practitioner. However, the acquisition of US images is operator-dependent, so it is unclear to what extent patients can acquire images that are suitable for remote diagnosis. In this paper, we present GAJA (Guided Acquisition of Joint ultrAsound), an application designed to guide the patient in collecting US images of their own joints, which are then transmitted to a medical practitioner. GAJA uses a collaborative interaction approach, in which an expert practitioner collects a reference US image of a specific scan during an in-person clinical visit. Anatomical markers for the target joint are automatically extracted and then used as a reference to guide the patient in properly positioning the US probe.
This work was partially supported by the project MUSA - Multilayered Urban Sustainability Action - project, funded by the European Union - NextGenerationEU, under the National Recovery and Resilience Plan (NRRP) Mission 4 Component 2 Investment Line 1.5: Strenghtening of research structures and creation of R &D “innovation ecosystems”, set up of “territorial leaders in R &D”, and by the Italian Ministry of Health - Bando Ricerca Corrente. The Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico is member of the European Reference Network (ERN) EuroBloodNet.
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Notes
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We use the term “patient” to denote the person in charge of acquiring the ultrasound images but actually it can be the patient or a caregiver.
- 2.
A scan is a specific view of a body part obtained by positioning the probe in a consistent way.
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- 4.
References
Aguero, P., Barnes, R.F., Flores, A., von Drygalski, A.: Teleguidance for patient self-imaging of hemophilic joints using mobile ultrasound devices: A pilot study. J. Ultrasound Med. 42(3), 701–712 (2023)
American College of Radiology (ACR), Society for Pediatric Radiology (SPR), Society of Radiologists in Ultrasound (SRU): AIUM practice guideline for the performance of a musculoskeletal ultrasound examination. J. Ultrasound Med. Official J. Am. Inst. Ultrasound Med. 31(9), 1473–1488 (2012)
Baribeau, Y., et al.: Handheld point-of-care ultrasound probes: the new generation of pocus. J. Cardiothorac. Vasc. Anesth. 34(11), 3139–3145 (2020)
Berlet, M., et al.: Emergency telemedicine mobile ultrasounds using a 5g-enabled application: development and usability study. JMIR Formative Res. 6(5), e36824 (2022)
Chiem, A.T., Lim, G.W., Tabibnia, A.P., Takemoto, A.S., Weingrow, D.M., Shibata, J.E.: Feasibility of patient-performed lung ultrasound self-exams (patient-plus) as a potential approach to telemedicine in heart failure. ESC Heart Failure 8(5), 3997–4006 (2021)
Colussi, M., et al.: Ultrasound detection of subquadricipital recess distension. Intelligent Systems with Applications p. 200183 (2023)
Corte, G., et al.: Performance of a handheld ultrasound device to assess articular and periarticular pathologies in patients with inflammatory arthritis. Diagnostics 11(7), 1139 (2021)
Culbertson, H., Walker, J.M., Raitor, M., Okamura, A.M., Stolka, P.J.: Plane Assist: The Influence of Haptics on Ultrasound-Based Needle Guidance. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 370–377. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46720-7_43
Duggan, N.M., et al.: Novice-performed point-of-care ultrasound for home-based imaging. Sci. Rep. 12(1), 20461 (2022)
Gualtierotti, R., et al.: A computer-aided diagnosis tool for the detection of hemarthrosis by remote joint ultrasound in patients with hemophilia. Blood 140(Supplement 1), 464–465 (2022)
Gualtierotti, R., Solimeno, L.P., Peyvandi, F.: Hemophilic arthropathy: current knowledge and future perspectives. Journal of Thrombosis and Haemostasis 19(9), 2112–2121 (2021)
Hilgartner, M.W.: Current treatment of hemophilic arthropathy. Current Opin. Pediatr. 14(1), 46–49 (2002)
Huang, Q., Zheng, Y., Lu, M., Chi, Z.: Development of a portable 3d ultrasound imaging system for musculoskeletal tissues. Ultrasonics 43(3), 153–163 (2005)
Jocher, G.: YOLOv5 by Ultralytics (2020). https://doi.org/10.5281/zenodo.3908559, https://github.com/ultralytics/yolov5
Kim, G.D.: A single FPGA-based portable ultrasound imaging system for point-of-care applications. IEEE Trans. Ultrasonics Ferroelectr. Freq. Control 59(7), 1386–1394 (2012)
McBeth, P.B., et al.: Simple, almost anywhere, with almost anyone: remote low-cost telementored resuscitative lung ultrasound. J. Trauma Acute Care Surg. 71(6), 1528–1535 (2011)
Plut, D., et al.: Diagnostic accuracy of haemophilia early arthropathy detection with ultrasound (head-us): a comparative magnetic resonance imaging (mri) study. Radiol. Oncol. 53(2), 178–186 (2019)
Roosendaal, G., Lafeber, F.P.: Blood-induced joint damage in hemophilia. In: Seminars in thrombosis and hemostasis. vol. 29, pp. 037–042. Copyright 2003 by Thieme Medical Publishers Inc, 333 Seventh Avenue, New... (2003)
Schneider, E., et al.: Can dialysis patients identify and diagnose pulmonary congestion using self-lung ultrasound? J. Clin. Med. 12(11), 3829 (2023)
Sun, S.Y., Gilbertson, M., Anthony, B.W.: Computer-guided ultrasound probe realignment by optical tracking. In: 2013 IEEE 10th International Symposium on Biomedical Imaging. pp. 21–24. IEEE (2013)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
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Colussi, M. et al. (2023). GAJA - Guided self-Acquisition of Joint ultrAsound images. In: Kainz, B., Noble, A., Schnabel, J., Khanal, B., Müller, J.P., Day, T. (eds) Simplifying Medical Ultrasound. ASMUS 2023. Lecture Notes in Computer Science, vol 14337. Springer, Cham. https://doi.org/10.1007/978-3-031-44521-7_13
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