Get an overview of what we are currently doing to improve and accelerate rare diseases diagnosis
Our publications
Keep up to date with Mendelian's research, read up on our latest publications, reports and posters to find out how our technology accelerates diagnosis and can help in reducing cost of care.
Lost in the System: The Labyrinth of Rare Disease Diagnosis
The journey to a rare disease diagnosis can be a maze. This insightful article published in the European Medical Journal explores the challenges patients face and offers a deeper understanding of the system.
The Diagnostic Odyssey in Children and Adolescents With XLH
The article, which appeared in the Journal of Clinical Endocrinology and Metabolism in July 2024, explores the recording of clinical features and the diagnostic odyssey of children and adolescents with X-linked hypophosphatemia (XLH) in primary care electronic healthcare records (EHRs) in the United Kingdom.
Identifying patients with undiagnosed small intestinal NETs
Published in the British Journal of Cancer, this study develops and validates a prediction model using primary care data to improve the early detection of undiagnosed small intestinal neuroendocrine tumours (NETs).
A Machine Learning Algorithm for the Detection of PNH
This study, published in Orphanet Journal of Rare Diseases, details the development of a machine learning algorithm designed to improve the detection of paroxysmal nocturnal haemoglobinuria (PNH) within UK primary care electronic health records, addressing the challenges of diagnosing this ultra-rare disorder.
Algorithmic Case Finding Approaches for Type 1 Gaucher Disease
This poster explores how case-finding algorithms can help doctors detect Type 1 Gaucher disease earlier in primary care, reducing diagnostic delays and improving patient outcomes.
Identifying Candidates for Genetic Testing in Inherited Retinal Diseases
This poster investigates how AI can analyse primary care electronic health records (EHRs) to identify patients with inherited retinal diseases (IRDs) who have been overlooked for genetic testing, enabling them to access the right treatments and clinical trials.
Using MendelScan to find undiagnosed FH patients
MendelScan uses AI to analyse electronic health records, helping doctors identify undiagnosed Familial Hypercholesterolemia (FH) patients early to reduce heart disease risk and improve care.
The Impact of MendelScan on DiGeorge Syndrome in the NHS
This poster presents research on using MendelScan to improve the diagnosis of DiGeorge Syndrome in the UK NHS, showing the potential for increased diagnosis rates, reduced healthcare costs, and fewer undiagnosed cases.
The Impact of MendelScan on Clinical Utilisation in the Diagnosis of PNH
This poster presents a simulation study evaluating the impact of MendelScan on the diagnosis of Paroxysmal Nocturnal Haemoglobinuria (PNH) within the UK's NHS, finding that it could significantly reduce diagnostic delays and costs.
Novel Partnership to address the diagnostic odyssey in PNH
This poster outlines a novel partnership using machine learning to improve the diagnosis of Paroxysmal Nocturnal Hemoglobinuria (PNH) and details the progress of case-finding technology through feasibility, optimisation, pilot, health resource analysis, and quality improvement stages.
MendelScan - AI for good: informing patient and public perception
Mendelian collaborated with a Patient and Public Involvement Group to enhance the communication and implementation of their AI-powered rare disease case-finding platform, MendelScan, within the NHS, as reported in Rare Revolution Magazine, a publication specialising in rare disease content.
AI in Health and Care Award Final Report
The AI in Health and Care Award funded a large-scale study of MendelScan between April 2023 and November 2024. This paper summarises an evaluation of MendelScan’s performance and potential impact on patients and the health system, and studies on the acceptability of using MendelScan in the NHS.
2022: Mendelian year in review
In 2022 MendelScan was used to scan the records of 672,026 real-world patients for over 40 diseases. Learn more about MendelScan and how our team of data scientists and clinicians use large, real-world data sets to isolate EHR patient phenotype signatures.
Reducing the diagnostic odyssey of HHT
We demonstrate how a digital health tool that scans EHRs may lead to an earlier diagnosis of Hereditary Hemorrhagic Telangiectasia
Health economic study
A preliminary assessment of the potential impact of rare diseases on the NHS. Rare diseases are an increasingly recognised health priority due to their impact, severity and burden on the patient, their family and the health system.
Ethics report
Ethical issues arising from the identification of patients with undiagnosed rare diseases from electronic patient records in prospective scenarios.
Early identification of rare diseases patients
Rare disease patients frecuently experience a significant delay in diagnosis. This study shows a novel digital approach, scanning Electronic Health Records at scale, may lead to earlier diagnosis.
Reducing the diagnostic odyssey
We show that a digital health approach may lead to earlier diagnosis by scanning Electronic Health Records (EHR), at scale, to identify patients with rare undiagnosed diseases.