Utility of 3D Facial Analysis As A Biomarker In Rare Diseases Exploration with Hereditary Angioedema
Journal:
medRxiv
Published Date:
Mar 26, 2026
Abstract
Importance: People living with rare diseases (PLWRD) often face significant challenges in receiving timely and accurate diagnoses, leading to what is known as a diagnostic odyssey. Digital phenotyping (DP) offers a promising solution by leveraging advanced technology, such as 3D facial photography, to capture unique digital signatures associated with various rare diseases. This innovative approach not only aids in the identification of these conditions but also facilitates the detection of digital biomarkers (DBM). These biomarkers enable healthcare providers to monitor the progression of the disease over time, enhancing patient care and potentially shortening the duration of the diagnostic odyssey. By utilizing DP and DBMs, we can improve both the diagnosis and management of RDs, ultimately leading to better health outcomes for affected participants. Objective: To identify whether DBMs can be identified by DP utilizing 3D facial imaging techniques in outpatient settings in participants with RDs. The primary objective of this study was to determine if specific facial measurements in participants with RD who experience transient episodes of facial swelling (oedema) differ from established ethnically matched norms. The secondary objective was to assess peri-orbital and/or facial swelling as a potential biomarker for identifying flare-ups in hereditary angioedema (HAE). Design, setting, and participants: This multicentre observational study was conducted in 3 hospitals in Singapore. The eligible participants were male and female RD participants of various age groups. The study duration was 4 years and 8 months. Interventions: Twenty participants of Chinese genetic ancestry were photographed using a 3D camera. Additionally, two participants with hereditary angioedema (HAE) were photographed during acute stages of disease flare-ups. Main outcomes and measures: The obtained facial scans of participants (that included participants with HAE in non-acute phase) were plotted using Artificial Intelligence-powered software - Cliniface. The growth curves and facial landmarks obtained were compared against the growth curves of normal RD-unaffected individuals of Chinese genetic ancestry. The two participants with HAE were photographed qualitatively over a longer period of time, and their scans were plotted, yielding growth curves. Results: Distinct facial markers such as periorbital swelling were identified in two qualitatively assessed HAE participants during flare-up stages. This provides an opportunity to explore and validate further if these facial signatures in a disease condition can be assigned as DBM for HAE. Conclusions and relevance: This study explores the utility of 3D facial analysis as a DBM in rare diseases such as HAE. Applying non-invasive signals coupled with AI may open new vistas for precision medicine in real-world settings.While DP's diagnostic capabilities may be limited, it successfully identified DBM, which could facilitate disease monitoring in conditions such as HAE.
