Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning

Kainz, B; Heinrich, MP; Makropoulos, A; Oppenheimer, J; Mandegaran, R; Sankar, S; Deane, C; Mischkewitz, S; Al-Noor, F; Rawdin, AC; Ruttloff, A; Stevenson, MD; Klein-Weigel, P; Curry, N

AI Collection

Journal article

Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning

Abstract:: Deep vein thrombosis (DVT) is a blood clot most commonly found in the leg, which can lead to fatal pulmonary embolism (PE). Compression ultrasound of the legs is the diagnostic gold standard, leading to a definitive diagnosis. However, many patients with possible symptoms are not found to have a DVT, resulting in long referral waiting times for patients and a large clinical burden for specialists. Thus, diagnosis at the point of care by non-specialists is desired. We collect images in a pre-clinical study and investigate a deep learning approach for the automatic interpretation of compression ultrasound images. Our method provides guidance for free-hand ultrasound and aids non-specialists in detecting DVT. We train a deep learning algorithm on ultrasound videos from 255 volunteers and evaluate on a sample size of 53 prospectively enrolled patients from an NHS DVT diagnostic clinic and 30 prospectively enrolled patients from a German DVT clinic. Algorithmic DVT diagnosis performance results in a sensitivity within a 95% CI range of (0.82, 0.94), specificity of (0.70, 0.82), a positive predictive value of (0.65, 0.89), and a negative predictive value of (0.99, 1.00) when compared to the clinical gold standard. To assess the potential benefits of this technology in healthcare we evaluate the entire clinical DVT decision algorithm and provide cost analysis when integrating our approach into diagnostic pathways for DVT. Our approach is estimated to generate a positive net monetary benefit at costs up to £72 to £175 per software-supported examination, assuming a willingness to pay of £20,000/QALY.

Publication status:: Published

Peer review status:: Peer reviewed

Actions

Email

Email this record

Send the bibliographic details of this record to your email address.

Your Email
Please enter the email address that the record information will be sent to.

-
Your message (optional)
Please add any additional information to be included within the email.
Share
Cite

Cite this record

APA Style

Kainz, B., Heinrich, M. P., Makropoulos, A., Oppenheimer, J., Mandegaran, R., Sankar, S., Deane, C., Mischkewitz, S., Al-Noor, F., Rawdin, A. C., Ruttloff, A., Stevenson, M. D., Klein-Weigel, P., & Curry, N. (2021). Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning. Npj Digital Medicine, 4(1), 137–137.

MLA Style

Kainz, B, et al. “Non-Invasive Diagnosis of Deep Vein Thrombosis from Ultrasound Imaging with Machine Learning.” Npj Digital Medicine, vol. 4, no. 1, 2021, pp. 137–37.

Chicago Style

Kainz, B, MP Heinrich, A Makropoulos, et al. 2021. “Non-Invasive Diagnosis of Deep Vein Thrombosis from Ultrasound Imaging with Machine Learning.” Npj Digital Medicine 4 (1): 137–37.
Print