TIDMSMD
RNS Number : 9203H
Spectral MD Holdings, Ltd.
09 August 2021
9 August 2021
Spectral MD Holdings, Ltd
("Spectral MD" or the "Company" or the Group")
Spectral MD hosts event to launch a multi-center clinical study
for burn wounds
Start of multi-center clinical study to further develop its
DeepView(R) Wound Imaging System and burn wound-healing prediction
AI Algorithm
LONDON, U.K AND DALLAS, TX, U.S - Spectral MD Holdings, Ltd.
(AIM:SMD), a predictive analytics company that develops proprietary
AI algorithms and optical technology for faster and more accurate
treatment decisions in wound care, hosted a successful two-day
kickoff event at its Dallas headquarters on 29 July - 30 July to
introduce the start of the Option 1A Training Study, a multi-center
clinical study that will support the further development of its
DeepView(R) Wound Imaging System.
Spectral MD welcomed physicians, clinical coordinators, and
researchers from five clinical candidate sites across the United
States to the kick-off event. Over the two-days, Spectral MD
showcased its next generation, user-friendly DeepView(R) technology
to physicians, clinical coordinators, and researchers from the five
candidate sites. The Spectral MD team participated in valuable
conversations and training with the physicians about the clinical
protocol, DeepView(R) in clinical practice, and further refinement
to the AI algorithm.
The DeepView(R) algorithm training study will build upon
promising results from the proof-of-concept study to advance the
technology of the DeepView(R) system, which has been granted FDA
Breakthrough Device Designation Status. The data collected from the
upcoming training study will be used to bolster the Company's
existing clinical database to train the AI algorithm and improve
the DeepView(R) technology in early burn wound healing assessment.
Study enrollment will initiate at the five clinical sites this
month and is expected to conclude in February 2022. The training
study will collect data from 100 adult and pediatric subjects to
further develop DeepView(R)'s AI algorithm.
The kick-off meeting is an important milestone for the Company
in fulfilling its contractual requirements with the Biomedical
Advanced Research and Development Authority (BARDA). BARDA is part
of the Office of the Assistant Secretary for Preparedness and
Response at the U.S Department of Health and Human Services. The
execution of the multi-center clinical training study is also a key
milestone highlighted as part of the Company's recent Initial
Public Offering and Admission to AIM. The Company is working to
provide BARDA and the United States government with a non-invasive
imaging platform that can accurately assist physicians with
instantaneous triage of burn patients in a mass-casualty event by
fitting Spectral MD's DeepView(R) into the continuum of burn
care.
Wensheng Fan, Spectral MD's Chief Executive Officer, said: "We
thank BARDA for their continued commitment and support. The
successful completion of this clinical trial represents a key
milestone in the development of the DeepView(R) technology."
Dr. Jeffrey Carter, Chief Medical Consultant, and National
Principal Investigator for the training study, stated: "I expect
this study to be a significant step in progressing the development
of this transformative technology. We will work with all the
participating clinical sites to ensure high quality data and
clinical engagement."
Dr. William Hickerson, the Medical Monitor for the upcoming
training study, and past President of the American Burn
Association, commented: "This important clinical study will provide
training data for Spectral MD's DeepView(R) AI algorithm
development. I look forward to working with Spectral MD, BARDA, and
all clinical investigators throughout this study."
Funding and technical support for development of the DeepView(R)
Wound Imaging System is provided by the Biomedical Advanced
Research and Development Authority (BARDA), under the Assistant
Secretary for Preparedness and Response (ASPR), within the U.S.
Department of Health and Human Services, under ongoing USG Contract
No. 75A50119C00033. For more information about BARDA, refer to
https://www.medicalcountermeasures.gov/ .
For further information please contact:
Spectral MD Holdings, Ltd. investors.spectralmd.com
Wensheng Fan, Chief Executive Officer via Walbrook PR
Wan Lung Eng, Chief Financial Officer
SP Angel Corporate Finance LLP (NOMAD Tel: +44 (0) 20 3470 0470
& BROKER)
Stuart Gledhill/ Caroline Rowe (Corporate
Finance)
Vadim Alexandre/Rob Rees (Sales & Broking)
Walbrook PR Ltd (Media & Investor Tel: +44 (0)20 7933 8780 or spectralMD@walbrookpr.com
Relations)
Paul McManus/Sam Allen Mob: +44 (0)7980 541 893 / +44 (0)7502
Alice Woodings 558 258
+44 (0)7407 804 654
About Spectral MD Holdings, Ltd. ( www.spectralmd.com )
Using its DeepView(R) Wound Imaging Solution, an internally
developed AI technology and multispectral imaging system which has
designated FDA Breakthrough Device, Spectral MD is able to
distinguish between damaged and healthy human tissue invisible to
the naked eye, providing 'Day One' healing assessments for burn
wounds and diabetic foot ulcers (DFU).
Spectral MD has to date received substantial support from the US
government with contracts from institutions such as Biomedical
Advanced Research and Development Authority (BARDA), National
Science Foundation (NSF), National Institute of Health (NIH) and
Defense Health Agency (DHA) in support of the burns application for
its DeepView(R) system.
The Company has one principal trading subsidiary, Spectral MD,
Inc., and has set up a subsidiary in the UK from which it will be
growing its business in the UK and EU.
DeepView(R)
DeepView(R) is a predictive analytics platform that combines AI
algorithms and medical imaging for wound prediction. It is
non-invasive, non-radiation, non-laser and does not require the use
of injectable dye. This integration can be characterised into four
distinct components: DeepView(R) imaging, data extraction, AI model
building and AI wound healing prediction.
-- The DeepView(R) imaging technology consists of patented
proprietary multi-spectral optics and sensors that can classify
wound tissue physiology and capture the viability of various
biomarkers within the skin. The imaging technology extracts
appropriate clinical data, processes the image and displays a
comparison of the original image next to an image with a colour
overlay of the non-healing portions of the wound. The image
acquisition takes 0.2 seconds and the output takes approximately 20
to 25 seconds.
-- DeepView(R)'s proprietary optics are able to extract millions
of data points or AI model features from each raw image. This
information is then used to build and continually improve the AI
model, which is trained and tested against a proprietary database
of more than 53 billion pixels with ever-growing
clinically-validated data points.
-- The AI algorithm then produces a wound healing prediction in
the form of an objective, accurate, and immediate binary wound
healing prediction. This prediction is graphically represented to
the clinician through a coloured overlay of the original image that
annotates the non-healing portion of the wound.
DeepView(R) is designed to allow clinicians to make a more
accurate, timely and informed decision regarding the treatment of
the patient's wound. In the case of DFUs, a non-healing assessment
would provide the physician with the appropriate justification to
use an advanced wound care therapy on 'Day One' as opposed to
waiting 30 days and potentially losing the patient to follow-up or
risking patient noncompliance with standard wound therapy. The
current clinical accuracy of DeepView(R) is 83 per cent. for DFUs.
For burn wounds, the clinician can make an immediate and objective
determination for appropriate candidates for surgery as well as
determining what specific areas of the burn wound will require skin
grafting. DeepView(R)'s current accuracy for burn wounds is 91 per
cent., compared with current physician accuracy of 50 per cent. to
70 per cent. DeepView(R) demonstrates a much higher diagnostic
accuracy for burn wounds and DFUs.
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