Mission
Utilize AI for advanced digital diagnostic tests on coeliac disease, enhancing pathology processes and elevating patient experience.
AI

AI-based diagnostic tests for coeliac disease

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Lyzeum's innovative AI technologies, built on the largest duodenal dataset globally, can support pathologists' workflow and improve accuracy and efficiency of diagnosis.
Our clinical grade AI improves the speed and accuracy of duodenal biopsy diagnosis, thereby reducing healthcare costs and improving patient outcomes.

Collaborators

Statistics
Expertise

A unique multi-disciplinary team of NHS pathologists, Cambridge University mathematicians and engineers dedicated to using machine learning to improve the accuracy and speed of diagnosis of coeliac disease (gluten sensitivity).

We work in conjunction with the Department of Pathology and the Centre for Mathematical Sciences at the University of Cambridge.
13 years
Coeliac disease diagnosis takes on average 13 years
3%
Only 3% of all pathology departments are fully staffed
80%
Agreement between pathologists when diagnosing coeliac disease is only 80%

We aim to be the first organisation to develop and deploy a fully automated solution for diagnosis in tissue-based pathology.

To achieve our goal, we are developing a generic solution for accurate and rapid diagnosis of duodenal (small intestinal) biopsies. This will help mitigate the current international shortage of diagnostic pathologists.
On average, it takes 13 years from the onset of symptoms to a diagnosis of coeliac disease. This is due to a combination of the possibility of coeliac disease not being considered and current tests lacking both sensitivity and specificity. Coeliac UK's website has more information.
Coeliac disease is readily treatable with a strict gluten-free diet. Symptoms do not go away if there is contamination in the diet. For more information, see Coeliac UK's website.
Most complications occur because the gluten-free diet is not strictly followed (sometimes because the patient does not yet have a diagnosis of coeliac disease). These include refractory coeliac disease type 2 (a type of pre-lymphoma), lymphoma and cancer of the small bowel.
Pathologists only agree on 80% of all coeliac diagnoses. We help to improve the quality of diagnosis.
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Quick facts

Frequent

At least 1% of the UK population (and most populations of European descent) have coeliac disease.

Missing

It is thought that for every one person who has a diagnosis of coeliac disease, 2 people do not. Some experts have suggested that that figure should be 12 not 2!

Treatable

Coeliac disease is readily treatable with a strict gluten-free diet. Symptoms do not go away if there is contamination in the diet. For more information, see Coeliac UK's website.

Testing

Current diagnosis in adults generally requires blood tests and an endoscopy with biopsy. Children may have blood tests only. The NICE guidelines have more information.

Slow

On average, it takes 13 years from the onset of symptoms to a diagnosis of coeliac disease.

Complications

Most complications occur because the gluten-free diet is not strictly followed. These include refractory coeliac disease type 2, lymphoma and small bowel cancer.

Our team

Professor
Elizabeth Soilleux
CEO
Dr Florian Jaeckle
CTO
Graham Snudden
CОО
Professor
Mark Arends
Scientific advisor
Professor
Carola-Bibiane Schönlieb
Scientific advisor

Publications

17 January 2024
BMJ Open Gastroenterology
CD, or not CD, that is the question - a digital inter-observer agreement study in coeliac disease
Coeliac disease (CD) diagnosis generally depends on histological examination of duodenal biopsies. We present the first study analysing the concordance in examination of duodenal biopsies using digitised whole-slide images (WSIs). We further investigate whether the inclusion of IgA tTG and haemoglobin (Hb)
Read more
03 January 2024
Scientific Reports
Rapid artefact removal and H&E-stained tissue segmentation
We present an innovative method for rapidly segmenting haematoxylin and eosin (H&E)-stained tissue in whole-slide images (WSIs) that eliminates a wide range of undesirable artefacts such as pen marks and scanning artefacts. Our method involves taking a single-channel representation of a low-magnification RGB overview of the WSI in which the pixel values are bimodally distributed such that H&E-stained tissue is easily distinguished from both background and a wide variety of artefacts. We demonstrate our method on 30 WSIs prepared from a wide range of institutions and WSI digital scanners, each containing substantial artefacts, and compare it to segmentations provided by Otsu thresholding and Histolab tissue segmentation and pen filtering tools. We found that our method segmented the tissue and fully removed all artefacts in 29 out of 30 WSIs, whereas Otsu thresholding failed to remove any artefacts, and the Histolab pen filtering tools only partially removed the pen marks. The beauty of our approach lies in its simplicity: manipulating RGB colour space and using Otsu thresholding allows for the segmentation of H&E-stained tissue and the rapid removal of artefacts without the need for machine learning or parameter tuning.
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19 July 2023
Journal of Pathology Informatics
Stain normalization gives greater generalizability than stain jittering in neural network training for the classification of coeliac disease in duodenal biopsy whole slide images
Around 1% of the population of the UK and North America have a diagnosis of coeliac disease (CD), due to a damaging immune response to the small intestine. Assessing whether a patient has CD relies primarily on the examination of a duodenal biopsy, an unavoidably subjective process with poor inter-observer concordance. Wei et al. [11] developed a neural network-based method for diagnosing CD using a dataset of duodenal biopsy whole slide images (WSIs).
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28 October 2022
Journal of Pathology Informatics
Multiple-instance-learning-based detection of coeliac disease in histological whole-slide images
We present a multiple-instance-learning-based scheme for detecting coeliac disease, an autoimmune disorder affecting the intestine, in histological whole-slide images (WSIs) of duodenal biopsies. We train our model to detect 2 distinct classes, normal tissue and coeliac disease, on the patch-level, and in turn leverage slide-level classifications. Using 5-fold cross-validation in a training set of 1841 (1163 normal; 680 coeliac disease) WSIs, our model classifies slides as normal with accuracy (96.7±0.6)%, precision (98.0±1.7)%, and recall (96.8±2.5)%, and as coeliac disease with accuracy (96.7±0.5)%, precision (94.9±3.7)%, and recall (96.5±2.9)% where the error bars are the cross-validation standard deviation.
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17 January 2024
BMJ Open Gastroenterology
CD, or not CD, that is the question - a digital inter-observer agreement study in coeliac disease
Coeliac disease (CD) diagnosis generally depends on histological examination of duodenal biopsies. We present the first study analysing the concordance in examination of duodenal biopsies using digitised whole-slide images (WSIs). We further investigate whether the inclusion of IgA tTG and haemoglobin (Hb)
Read more
17 January 2024
BMJ Open Gastroenterology
CD, or not CD, that is the question - a digital inter-observer agreement study in coeliac disease
Coeliac disease (CD) diagnosis generally depends on histological examination of duodenal biopsies. We present the first study analysing the concordance in examination of duodenal biopsies using digitised whole-slide images (WSIs). We further investigate whether the inclusion of IgA tTG and haemoglobin (Hb)
Read more
17 January 2024
BMJ Open Gastroenterology
CD, or not CD, that is the question - a digital inter-observer agreement study in coeliac disease
Coeliac disease (CD) diagnosis generally depends on histological examination of duodenal biopsies. We present the first study analysing the concordance in examination of duodenal biopsies using digitised whole-slide images (WSIs). We further investigate whether the inclusion of IgA tTG and haemoglobin (Hb)
Read more
17 January 2024
BMJ Open Gastroenterology
CD, or not CD, that is the question - a digital inter-observer agreement study in coeliac disease
Coeliac disease (CD) diagnosis generally depends on histological examination of duodenal biopsies. We present the first study analysing the concordance in examination of duodenal biopsies using digitised whole-slide images (WSIs). We further investigate whether the inclusion of IgA tTG and haemoglobin (Hb)
Read more
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