Our connected ecosystem
Our patented technologies work together to provide quick and accurate test results wherever they are needed, within one minute.
Our connected ecosystem
Our patented technologies work together to provide quick and accurate test results wherever they are needed, within one minute.
Our connected ecosystem
Our patented technologies work together to provide quick and accurate test results wherever they are needed, within one minute.
Our connected ecosystem
Our patented technologies work together to provide quick and accurate test results wherever they are needed, within one minute.
Our connected ecosystem
Our patented technologies work together to provide quick and accurate test results wherever they are needed, within one minute.
How It Works
How It Works
How It Works
How It Works
How It Works
PIC-ID Capture is a proprietary labelling reagent that binds to anything surrounded by a biological membrane. Upon instantaneous binding, each pathogen has a unique fluorescent signature, facilitating specific and sensitive optical analysis.
PIC-ID Capture is a proprietary labelling reagent that binds to anything surrounded by a biological membrane. Upon instantaneous binding, each pathogen has a unique fluorescent signature, facilitating specific and sensitive optical analysis.
PIC-ID Capture is a proprietary labelling reagent that binds to anything surrounded by a biological membrane. Upon instantaneous binding, each pathogen has a unique fluorescent signature, facilitating specific and sensitive optical analysis.
PIC-ID Capture is a proprietary labelling reagent that binds to anything surrounded by a biological membrane. Upon instantaneous binding, each pathogen has a unique fluorescent signature, facilitating specific and sensitive optical analysis.
PIC-ID Capture is a proprietary labelling reagent that binds to anything surrounded by a biological membrane. Upon instantaneous binding, each pathogen has a unique fluorescent signature, facilitating specific and sensitive optical analysis.
Patent 1 - PCT/GB2019/053073
Patent 1 - PCT/GB2019/053073
Patent 1 - PCT/GB2019/053073
Patent 1 - PCT/GB2019/053073
Patent 1 - PCT/GB2019/053073
VISTA Reader, a specialised instrument, uses fluorescent microscopy to capture digital images of pathogens that have been tagged with the PIC-ID Capture reagent and processes the images for analysis.
VISTA Reader, a specialised instrument, uses fluorescent microscopy to capture digital images of pathogens that have been tagged with the PIC-ID Capture reagent and processes the images for analysis.
VISTA Reader, a specialised instrument, uses fluorescent microscopy to capture digital images of pathogens that have been tagged with the PIC-ID Capture reagent and processes the images for analysis.
VISTA Reader, a specialised instrument, uses fluorescent microscopy to capture digital images of pathogens that have been tagged with the PIC-ID Capture reagent and processes the images for analysis.
VISTA Reader, a specialised instrument, uses fluorescent microscopy to capture digital images of pathogens that have been tagged with the PIC-ID Capture reagent and processes the images for analysis.
PIC-ID Identify is our proprietary data pre-processing and machine learning algorithm that integrates advanced image pre-processing techniques and deep learning to analyse and classify pathogens in the digital images captured by VISTA Reader.
PIC-ID Identify is our proprietary data pre-processing and machine learning algorithm that integrates advanced image pre-processing techniques and deep learning to analyse and classify pathogens in the digital images captured by VISTA Reader.
PIC-ID Identify is our proprietary data pre-processing and machine learning algorithm that integrates advanced image pre-processing techniques and deep learning to analyse and classify pathogens in the digital images captured by VISTA Reader.
PIC-ID Identify is our proprietary data pre-processing and machine learning algorithm that integrates advanced image pre-processing techniques and deep learning to analyse and classify pathogens in the digital images captured by VISTA Reader.
PIC-ID Identify is our proprietary data pre-processing and machine learning algorithm that integrates advanced image pre-processing techniques and deep learning to analyse and classify pathogens in the digital images captured by VISTA Reader.
Patent 2 - PCT/GB2021/050990 © - Preprocessing and deep learning software
Patent 2 - PCT/GB2021/050990 © - Preprocessing and deep learning software
Patent 2 - PCT/GB2021/050990 © - Preprocessing and deep learning software
Patent 2 - PCT/GB2021/050990 © - Preprocessing and deep learning software
Patent 2 - PCT/GB2021/050990 © - Preprocessing and deep learning software
Deep learning is used to train models to recognise specific viruses using positive samples.
Deep learning is used to train models to recognise specific viruses using positive samples.
Deep learning is used to train models to recognise specific viruses using positive samples.
Deep learning is used to train models to recognise specific viruses using positive samples.
Deep learning is used to train models to recognise specific viruses using positive samples.
Suspected viruses are snipped from images of the sample
Suspected viruses are snipped from images of the sample
Suspected viruses are snipped from images of the sample
Suspected viruses are snipped from images of the sample
Suspected viruses are snipped from images of the sample
Snipped images are compared against reference images in the trained models.
Snipped images are compared against reference images in the trained models.
Snipped images are compared against reference images in the trained models.
Snipped images are compared against reference images in the trained models.
Snipped images are compared against reference images in the trained models.
Product Workflow
The Science
The Science
The Science
The Science
The Science
Virus Detection and Identification in Minutes Using Single-Particle Imaging and Deep Learning
Virus Detection and Identification in Minutes Using Single-Particle Imaging and Deep Learning
Virus Detection and Identification in Minutes Using Single-Particle Imaging and Deep Learning
Virus Detection and Identification in Minutes Using Single-Particle Imaging and Deep Learning
Virus Detection and Identification in Minutes Using Single-Particle Imaging and Deep Learning
Published in ACS Nano | December 21, 2022
Published in ACS Nano | December 21, 2022
Published in ACS Nano | December 21, 2022
Published in ACS Nano | December 21, 2022
Published in ACS Nano | December 21, 2022
Fluorescent labelling was achieved within seconds via a single-step addition of labelling mixture, after which the viruses were immediately immobilised.
Proof of principle experiments showed that a convolutional neural network (CNN) can distinguish between avian coronavirus IBV and various strains of influenza with high accuracies of >90% per particle.
Sample accuracies of 97-98% from 155 patient samples.
Demonstrates end-to-end workflow in <5 mins.
Fluorescent labelling was achieved within seconds via a single-step addition of labelling mixture, after which the viruses were immediately immobilised.
Proof of principle experiments showed that a convolutional neural network (CNN) can distinguish between avian coronavirus IBV and various strains of influenza with high accuracies of >90% per particle.
Sample accuracies of 97-98% from 155 patient samples.
Demonstrates end-to-end workflow in <5 mins.
Fluorescent labelling was achieved within seconds via a single-step addition of labelling mixture, after which the viruses were immediately immobilised.
Proof of principle experiments showed that a convolutional neural network (CNN) can distinguish between avian coronavirus IBV and various strains of influenza with high accuracies of >90% per particle.
Sample accuracies of 97-98% from 155 patient samples.
Demonstrates end-to-end workflow in <5 mins.
Fluorescent labelling was achieved within seconds via a single-step addition of labelling mixture, after which the viruses were immediately immobilised.
Proof of principle experiments showed that a convolutional neural network (CNN) can distinguish between avian coronavirus IBV and various strains of influenza with high accuracies of >90% per particle.
Sample accuracies of 97-98% from 155 patient samples.
Demonstrates end-to-end workflow in <5 mins.
Fluorescent labelling was achieved within seconds via a single-step addition of labelling mixture, after which the viruses were immediately immobilised.
Proof of principle experiments showed that a convolutional neural network (CNN) can distinguish between avian coronavirus IBV and various strains of influenza with high accuracies of >90% per particle.
Sample accuracies of 97-98% from 155 patient samples.
Demonstrates end-to-end workflow in <5 mins.
Competitive advantage
Competitive advantage
Competitive advantage
Competitive advantage
Competitive advantage
Diagnostics today are too slow
Diagnostics today are too slow
Diagnostics today are too slow
Diagnostics today are too slow
Diagnostics today are too slow
Diagnostic testing in central labs is too slow to provide actionable results where and when they are needed - at the point of care.
Extended turnaround times due to transportation of samples to specialised labs separates the test results from the patient's clinical exam, creates additional follow-up steps for providers, and lengthens time to treatment.
To date, no true solution exists, and existing point of care tests provide results in >15 mins. Given that the average GP appointment in the UK lasts about 10 minutes time is still a major issue for the wider adoption of POC testing. Providers still need to give the patient a follow-up call to communicate results, sometimes hours or days later.
Diagnostic testing in central labs is too slow to provide actionable results where and when they are needed - at the point of care.
Extended turnaround times due to transportation of samples to specialised labs separates the test results from the patient's clinical exam, creates additional follow-up steps for providers, and lengthens time to treatment.
To date, no true solution exists, and existing point of care tests provide results in >15 mins. Given that the average GP appointment in the UK lasts about 10 minutes time is still a major issue for the wider adoption of POC testing. Providers still need to give the patient a follow-up call to communicate results, sometimes hours or days later.
Diagnostic testing in central labs is too slow to provide actionable results where and when they are needed - at the point of care.
Extended turnaround times due to transportation of samples to specialised labs separates the test results from the patient's clinical exam, creates additional follow-up steps for providers, and lengthens time to treatment.
To date, no true solution exists, and existing point of care tests provide results in >15 mins. Given that the average GP appointment in the UK lasts about 10 minutes time is still a major issue for the wider adoption of POC testing. Providers still need to give the patient a follow-up call to communicate results, sometimes hours or days later.
Diagnostic testing in central labs is too slow to provide actionable results where and when they are needed - at the point of care.
Extended turnaround times due to transportation of samples to specialised labs separates the test results from the patient's clinical exam, creates additional follow-up steps for providers, and lengthens time to treatment.
To date, no true solution exists, and existing point of care tests provide results in >15 mins. Given that the average GP appointment in the UK lasts about 10 minutes time is still a major issue for the wider adoption of POC testing. Providers still need to give the patient a follow-up call to communicate results, sometimes hours or days later.
Diagnostic testing in central labs is too slow to provide actionable results where and when they are needed - at the point of care.
Extended turnaround times due to transportation of samples to specialised labs separates the test results from the patient's clinical exam, creates additional follow-up steps for providers, and lengthens time to treatment.
To date, no true solution exists, and existing point of care tests provide results in >15 mins. Given that the average GP appointment in the UK lasts about 10 minutes time is still a major issue for the wider adoption of POC testing. Providers still need to give the patient a follow-up call to communicate results, sometimes hours or days later.
PIC ID against today's market leaders
PIC ID against today's market leaders
PIC ID against today's market leaders
PIC ID against today's market leaders
PIC ID against today's market leaders
Technology
Lateral flow
PCR
Nucleic Acid Amplification
Phenotype Image Capture (PIC-ID)
Specificity
50-80%
>98%
98%
98%
Actionable Result During Exams
Multiplex detection
Swipe left to view table
Technology
Lateral flow
PCR
Nucleic Acid Amplification
Phenotype Image Capture (PIC-ID)
Specificity
50-80%
>98%
98%
98%
Actionable Result During Exams
Multiplex detection
Technology
Lateral flow
PCR
Nucleic Acid Amplification
Phenotype Image Capture (PIC-ID)
Specificity
50-80%
>98%
98%
98%
Actionable Result During Exams
Multiplex detection
Swipe left to view table
Technology
Lateral flow
PCR
Nucleic Acid Amplification
Phenotype Image Capture (PIC-ID)
Specificity
50-80%
>98%
98%
98%
Actionable Result During Exams
Multiplex detection
Technology
Lateral flow
PCR
Nucleic Acid Amplification
Phenotype Image Capture (PIC-ID)
Specificity
50-80%
>98%
98%
98%
Actionable Result During Exams
Multiplex detection
Combating high infection rate pathogens
Combating high infection rate pathogens
Combating high infection rate pathogens
Combating high infection rate pathogens
Combating high infection rate pathogens
The high demand for testing during COVID-19 pandemic laid bare the deficiencies in the traditional infectious disease testing model even with the latest innovations.
Laboratories did not have the capacity to test all samples early in the pandemic. Rapid PCR point of care testing was still not fast enough to avoid potentially infected people queuing to get tested or requiring uninfected people to isolate while they waited 24 hours for results.
Lateral flow tests filled the gap out of necessity, but they sacrificed performance for speed to result (approx. 15 min) and were not fully trusted like laboratory results were.
With Pictura Bio’s technology, all of these challenges will be addressed directly.
The high demand for testing during COVID-19 pandemic laid bare the deficiencies in the traditional infectious disease testing model even with the latest innovations.
Laboratories did not have the capacity to test all samples early in the pandemic. Rapid PCR point of care testing was still not fast enough to avoid potentially infected people queuing to get tested or requiring uninfected people to isolate while they waited 24 hours for results.
Lateral flow tests filled the gap out of necessity, but they sacrificed performance for speed to result (approx. 15 min) and were not fully trusted like laboratory results were.
With Pictura Bio’s technology, all of these challenges will be addressed directly.
The high demand for testing during COVID-19 pandemic laid bare the deficiencies in the traditional infectious disease testing model even with the latest innovations.
Laboratories did not have the capacity to test all samples early in the pandemic. Rapid PCR point of care testing was still not fast enough to avoid potentially infected people queuing to get tested or requiring uninfected people to isolate while they waited 24 hours for results.
Lateral flow tests filled the gap out of necessity, but they sacrificed performance for speed to result (approx. 15 min) and were not fully trusted like laboratory results were.
With Pictura Bio’s technology, all of these challenges will be addressed directly.
The high demand for testing during COVID-19 pandemic laid bare the deficiencies in the traditional infectious disease testing model even with the latest innovations.
Laboratories did not have the capacity to test all samples early in the pandemic. Rapid PCR point of care testing was still not fast enough to avoid potentially infected people queuing to get tested or requiring uninfected people to isolate while they waited 24 hours for results.
Lateral flow tests filled the gap out of necessity, but they sacrificed performance for speed to result (approx. 15 min) and were not fully trusted like laboratory results were.
With Pictura Bio’s technology, all of these challenges will be addressed directly.
The high demand for testing during COVID-19 pandemic laid bare the deficiencies in the traditional infectious disease testing model even with the latest innovations.
Laboratories did not have the capacity to test all samples early in the pandemic. Rapid PCR point of care testing was still not fast enough to avoid potentially infected people queuing to get tested or requiring uninfected people to isolate while they waited 24 hours for results.
Lateral flow tests filled the gap out of necessity, but they sacrificed performance for speed to result (approx. 15 min) and were not fully trusted like laboratory results were.
With Pictura Bio’s technology, all of these challenges will be addressed directly.
Get in touch to find out more
Our technology will change diagnostics and healthcare, everywhere, to find out how you can be a part of our journey, come and talk to us.