Artificial Intelligence (AI)-Assisted Readout Method for the Evaluation of Skin Prick Automated Test Results (2025)

Preprints with The Lancet is a collaboration between The Lancet Group of journals and SSRN to facilitate the open sharing of preprints for early engagement, community comment, and collaboration. Preprints available here are not Lancet publications or necessarily under review with a Lancet journal. These preprints are early-stage research papers that have not been peer-reviewed. The usual SSRN checks and a Lancet-specific check for appropriateness and transparency have been applied. The findings should not be used for clinical or public health decision-making or presented without highlighting these facts. For more information, please see the FAQs.

22 PagesPosted: 16 Dec 2024

See all articles by Sven F. Seys

Sven F. Seys

Hippo Dx

Valérie Hox

Catholic University of Louvain (UCL) - Cliniques Universitaires Saint-Luc

Adam Chaker

Technische Universität München (TUM)

Glynnis De Greve

GZA Sint-Augustinus

Winde Lemmens

Hospital Oost-Limburg

Anne-Lise Poirrier

University of Liège

Eline Beckers

Hospital Oost-Limburg

Rembert Daems

Hippo Dx

Zuzana Diamant

KU Leuven

Carmen Dierickx

Hospital Oost-Limburg

Peter W. Hellings

KU Leuven

Caroline Huart

Catholic University of Louvain (UCL) - Cliniques Universitaires Saint-Luc

Claudia Jerin

Technische Universität München (TUM)

Mark Jorissen

UZ Leuven

Dirk Loeckx

Hippo Dx

Hanne Ocsé

GZA Sint-Augustinus

Karolien Roux

University of Groningen

Mark Thompson

Zurich University of Applied Sciences

Sophie Tombu

University of Liège

Saartje Uyttebroek

UZ Leuven

Andrzej Zarowski

GZA Sint-Augustinus

Senne Gorris

Hippo Dx

Laura Van Gerven

UZ Leuven

More...

Abstract

Background: Theskin prick test (SPT) is the gold standard for diagnosing allergic sensitization to aeroallergies. A novel device, Skin Prick Automated Test (SPAT), has previously demonstrated reduced variability and more consistent test results compared to manual SPT. The current study aimed to develop and validate an artificial intelligence (AI) assisted readout method to support physicians in interpreting skin reactions following SPAT.

Methods: 963 patients with suspected aeroallergies underwent SPT using SPAT for ten common allergens. To train and validate the AI algorithm, respectively 7812 (651 patients, 75%) and 2604 (217 patients, 25%) wheals were manually annotated by a person blinded to the outcome of the AI. The longest wheal diameter was measured by the treating physician and compared to the AI measurement. The AI-assisted readout was validated on a separate test cohort of 95 patients (1140 wheals).

Findings: The AI measurements of the longest wheal diameter exhibited a strong correlation with the physician’s measurements. The AI algorithm showed a specificity of 98.4% and sensitivity of 85·0% in determining positive or negative test results in the validation cohort. In the test cohort, physicians adjusted 5·8% of AI measurements, leading to a change in the test interpretation for only 0·5% of cases. AI-assisted readout significantly reduced inter- and intra-observer variability and readout time compared to manual physician measurements.

Interpretation: The AI-assisted readout software demonstrated high accuracy, with minimal misclassification of test results. Adding AI to SPAT further improved standardization across the SPT process, significantly reducing observer variability and time to readout.

Trial Registration: This study wasregistered online at www.clinicaltrials.gov (NCT05918354).

Funding:The study was funded by Hippocreates BV. LVG was supported by post-doctoral grants from 86 the University Hospitals Leuven (KOOR-UZ Leuven) and by the Research Foundation Flanders 87 (FWO) Senior Clinical Investigator Fellowship (18B2222N).

Declaration of Interest:MJT received consulting fees for statistical advice for the study. SFS, RD, DL are employees of Hippocreates BV. SFS, RD, DL, SG and LVG hold shares of Hippocreates BV. ZD received speaker or consultant honoraria and/or served on advisory boards (past 36 months) at: Antabio, Arcede, Biosion, Foresee Pharmaceuticals, Galenus Health, GlaxoSmithKline, Hippo43 Dx, Pleuran, QPS-Netherlands, Sanofi-Genzyme-Regeneron. During the last 3 years of her assignment as Research Director Respiratory and Allergy at QPS-Netherlands, the company received an European grant from ERA4TB and funding from Foresee Pharmaceuticals for early clinical studies. ZD serves/ed as associate editor for Allergy (2018-2023) and Respiratory Medicine (ongoing) and acted as Chair of the Asthma Section at EAACI (2017-2019) and Expert Panel at EUFOREA (2020-2024). AMC reports grants, speaker honoraria, consultancy or advisory fees and/or research support and other, all via Technical University of Munich from Allergopharma, ALK Abello, Astra Zeneca, Bencard/Allergen Therapeutics, GSK, Novartis, Hippo Dx, LETI, Roche, Zeller, Sanofi, Regeneron, Thermo Fisher, European Institute of Technology (EIT Health) and Federal Ministry of Research and Education Germany. Other authors have nothing to disclose related to this study.

Ethical Approval: The study was approved by the institutional review boards.

Keywords: Skin prick test, diagnosis, type I hypersensitivity, allergy, skin prick automated test, machine learning, artificial intelligence, medical device

Suggested Citation:Suggested Citation

Seys, Sven F. and Hox, Valérie and Chaker, Adam and De Greve, Glynnis and Lemmens, Winde and Poirrier, Anne-Lise and Beckers, Eline and Daems, Rembert and Diamant, Zuzana and Dierickx, Carmen and Hellings, Peter W. and Huart, Caroline and Jerin, Claudia and Jorissen, Mark and Loeckx, Dirk and Ocsé, Hanne and Roux, Karolien and Thompson, Mark and Tombu, Sophie and Uyttebroek, Saartje and Zarowski, Andrzej and Gorris, Senne and Van Gerven, Laura, Artificial Intelligence (AI)-Assisted Readout Method for the Evaluation of Skin Prick Automated Test Results. Available at SSRN: https://ssrn.com/abstract=5054180 or http://dx.doi.org/10.2139/ssrn.5054180

Sven F. Seys (Contact Author)

Hippo Dx ( email )

Valérie Hox

Catholic University of Louvain (UCL) - Cliniques Universitaires Saint-Luc ( email )

Belgium

Adam Chaker

Technische Universität München (TUM) ( email )

Glynnis De Greve

GZA Sint-Augustinus ( email )

Winde Lemmens

Hospital Oost-Limburg ( email )

Anne-Lise Poirrier

University of Liège ( email )

Eline Beckers

Hospital Oost-Limburg ( email )

Rembert Daems

Hippo Dx ( email )

Zuzana Diamant

KU Leuven ( email )

Carmen Dierickx

Hospital Oost-Limburg ( email )

Peter W. Hellings

KU Leuven ( email )

Caroline Huart

Catholic University of Louvain (UCL) - Cliniques Universitaires Saint-Luc ( email )

Belgium

Claudia Jerin

Technische Universität München (TUM) ( email )

Mark Jorissen

UZ Leuven ( email )

Dirk Loeckx

Hippo Dx ( email )

Hanne Ocsé

GZA Sint-Augustinus ( email )

Karolien Roux

University of Groningen ( email )

Mark Thompson

Zurich University of Applied Sciences ( email )

Sophie Tombu

University of Liège ( email )

Saartje Uyttebroek

UZ Leuven ( email )

Andrzej Zarowski

GZA Sint-Augustinus ( email )

Senne Gorris

Hippo Dx ( email )

Laura Van Gerven

UZ Leuven ( email )

Artificial Intelligence (AI)-Assisted Readout Method for the Evaluation of Skin Prick Automated Test Results (2025)

References

Top Articles
Latest Posts
Recommended Articles
Article information

Author: Gregorio Kreiger

Last Updated:

Views: 5499

Rating: 4.7 / 5 (57 voted)

Reviews: 88% of readers found this page helpful

Author information

Name: Gregorio Kreiger

Birthday: 1994-12-18

Address: 89212 Tracey Ramp, Sunside, MT 08453-0951

Phone: +9014805370218

Job: Customer Designer

Hobby: Mountain biking, Orienteering, Hiking, Sewing, Backpacking, Mushroom hunting, Backpacking

Introduction: My name is Gregorio Kreiger, I am a tender, brainy, enthusiastic, combative, agreeable, gentle, gentle person who loves writing and wants to share my knowledge and understanding with you.