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Founded Year



Series B | Alive

Total Raised


Last Raised

$33M | 2 yrs ago

About Ultromics

Ultromics provides autonomous echocardiography analysis through artificial intelligence (AI) solutions, empowering physicians to make decisions when diagnosing cardiovascular disease. Its cloud-based service, EchoGo, uses artificial intelligence to fully automate the pathway to diagnosis, providing reports for clinicians without any need for physical software on-site. The company was founded in 2017 and is based in Oxford, United Kingdom.

Headquarters Location

4630 Kingsgate Cascade Way

Oxford, England, OX4 2SU,

United Kingdom


ESPs containing Ultromics

The ESP matrix leverages data and analyst insight to identify and rank leading companies in a given technology landscape.

Healthcare & Life Sciences / Monitoring, Imaging & Diagnostics Tech

The portable ultrasounds market provides healthcare professionals with the ability to perform ultrasound exams in a variety of settings, including physician offices, clinics, hospitals, and even in the field. Portable ultrasound systems offer the same imaging quality as traditional systems, but in a much smaller and more convenient package. Many vendors use AI algorithms to automate the imaging pr…

Ultromics named as Leader among 10 other companies, including Clarius Mobile Health, EchoNous, and Exo.

Ultromics's Products & Differentiators


    Ultromics’ products operate through the cloud, as a Software-as-a-Service (SaaS) connected with Microsoft Azure cloud. Once connected, starting scanning, and let Ultromics take care of analysis. We’ll provides reports without the need to for any physical software or manual interaction, with results trusted globally by sites including Mayo Clinic, Cleveland Clinic, and the American Society of Echocardiography.


Research containing Ultromics

Get data-driven expert analysis from the CB Insights Intelligence Unit.

CB Insights Intelligence Analysts have mentioned Ultromics in 3 CB Insights research briefs, most recently on Jun 6, 2023.

Expert Collections containing Ultromics

Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.

Ultromics is included in 3 Expert Collections, including Artificial Intelligence.


Artificial Intelligence

10,958 items

Companies developing artificial intelligence solutions, including cross-industry applications, industry-specific products, and AI infrastructure solutions.


Digital Health

10,585 items

The digital health collection includes vendors developing software, platforms, sensor & robotic hardware, health data infrastructure, and tech-enabled services in healthcare. The list excludes pureplay pharma/biopharma, sequencing instruments, gene editing, and assistive tech.


Digital Health 150

150 items

The winners of the third annual CB Insights Digital Health 150.

Ultromics Patents

Ultromics has filed 1 patent.

The 3 most popular patent topics include:

  • artificial intelligence
  • artificial neural networks
  • cardiology
patents chart

Application Date

Grant Date


Related Topics



Artificial neural networks, Medical ultrasonography, Cardiology, Artificial intelligence, Cardiovascular physiology


Application Date


Grant Date


Related Topics

Artificial neural networks, Medical ultrasonography, Cardiology, Artificial intelligence, Cardiovascular physiology



Latest Ultromics News

AI shows ‘technical and clinical feasibility’ to detect HFpEF from a TTE video clip

Aug 10, 2023

Disclosures: This study received grant support from Ultromics Ltd. Akerman is an employee of Ultromics Ltd. Gevaert reports receiving lecture/advisory board fees paid to his institution by Abbott, AstraZeneca, Boehringer Ingelheim, Menarini and Novartis. Please see the study and editorial for all other authors’ relevant financial disclosures. ADD TOPIC TO EMAIL ALERTS Receive an email when new articles are posted on Please provide your email address to receive an email when new articles are posted on . Please try again later. If you continue to have this issue please contact . Back to Healio Key takeaways: An AI model has been designed to diagnose HF with preserved ejection fraction from a single transthoracic echocardiogram video clip. The algorithm demonstrated high levels of sensitivity and specificity. Artificial intelligence demonstrated strong diagnostic performance in the detection of HF with preserved ejection fraction using a single transthoracic echocardiogram video clip, according to data published in JACC: Advances. “ Recent work in artificial intelligence (AI) computer vision techniques offer great promise that computational methods can better interpret the vast amount of information that exists within medical data including images. Whereas recent AI studies have combined clinical parameters and manual echocardiographic measurements to classify diastolic dysfunction and HFpEF, fewer have used echocardiographic images,” Ashley P. Akerman, PhD, senior clinical research scientist at Ultromics Ltd. in Oxford, U.K., and colleagues wrote. “Development of an approach using this simple input might obviate the need for complex Doppler assessment, provide supporting information when traditional measures are nondiagnostic, or limit data requirements when such data collection is not feasible.” An AI model has been designed to diagnose HFpEF from a single transthoracic echocardiogram video clip. Image: Adobe Stock Akerman and colleagues trained and validated a 3D convolutional neural network AI using apical four-chamber transthoracic echocardiogram (TTE) video clips from 2,971 patients with HFpEF and 3,785 controls. “This view was selected because it includes much information (chamber sizes, wall thicknesses, annulus motion, etc) and is routinely acquired in imaging protocols,” the researchers wrote. AI training, validation and performance The AI model had an area under receiver-operating characteristic curve (AUROC) of 0.97 (95% CI, 0.96-0.97) in training and an AUROC of 0.95 (95% CI, 0.93-0.96) in validation. In independent testing conducted using 646 cases of HFpEF and 638 controls, the AI demonstrated a sensitivity to detect HFpEF of 87.8% (95% CI, 84.5-90.9) and a specificity of 81.9% (95% CI, 78.2-85.6) with high repeatability and reproducibility, according to the researchers. Furthermore, performance was assessed in an independent dataset in which the diagnostic value of the AI was compared with the previously validated Heart Failure Association-Pretest Assessment, Echocardiographic and Natriuretic Peptide Score, Functional Testing, and Final Etiology (HFA-PEFF) and Heavy, Hypertensive, Atrial Fibrillation, Pulmonary Hypertension, Elder and Filling Pressure (H2FPEF) scores. The AI correctly detected HFpEF among 73.5% of 701 indeterminate outputs from the HFA-PEFF score and 73.6% of 776 indeterminate outputs from the H2FPEF score, according to the study. During a median follow-up of 2.3 years, the researchers observed increased mortality risk among patients with HFpEF as determined by the AI compared with patients in whom the AI did not report HFpEF (HR = 1.9; 95% CI, 1.5-2.4). “The ability to automatically detect HFpEF with limited clinical information has important practical ramifications, particularly for screening in centers without the time or expertise to complete diagnostic quality diastolic assessment, resulting in indeterminate or unclear clinical diagnoses,” the researchers wrote. “Combined, the technical and clinical feasibility demonstrated with this model could result in faster patient access to effective pharmacological therapy.” AI as a ‘second read’ when other metrics uncertain In a related editorial, Andreas B. Gevaert, MD, PhD, postdoctoral researcher in the department of genetics, pharmacology and physiopathology of heart, blood vessels and skeleton at the University of Antwerp, and colleagues discussed the AI model’s performance compared with the two validated scoring systems and the algorithm’s place in clinical decision-making. “The better performance of the model compared to the scoring systems needs to be interpreted with caution,” the authors wrote. “Natriuretic peptide levels are an essential part of the HFA-PEFF score. However, these were only available in 12% of the validation population, implying that a diagnosis of HFpEF could not be established anyway in the majority of patients using this score. Both scoring systems mandate further (exercise) testing in patients labeled as ‘intermediate’ risk, which was not accounted for in the current study. “As the authors imply in their paper, the ideal use for this algorithm (or similar AI applications) would be as a ‘second read’ when other clinical or echocardiographic metrics remain uncertain regarding a diagnosis of HFpEF,” they wrote. “AI could serve as a ‘decision maker’ in intermediate or indeterminate cases and/or a ‘gatekeeper’ for advanced exercise testing to enhance early HFpEF diagnosis and enable timely treatment.” Reference:

Ultromics Frequently Asked Questions (FAQ)

  • When was Ultromics founded?

    Ultromics was founded in 2017.

  • Where is Ultromics's headquarters?

    Ultromics's headquarters is located at 4630 Kingsgate, Oxford.

  • What is Ultromics's latest funding round?

    Ultromics's latest funding round is Series B.

  • How much did Ultromics raise?

    Ultromics raised a total of $57.7M.

  • Who are the investors of Ultromics?

    Investors of Ultromics include Oxford Science Enterprises, Optum Ventures, The Blue Venture Fund, Google Ventures, Nina Capital and 12 more.

  • Who are Ultromics's competitors?

    Competitors of Ultromics include Kheiron Medical Technologies, DiA Imaging Analysis, Caption Health,, Aidoc and 7 more.

  • What products does Ultromics offer?

    Ultromics's products include EchoGo.


Compare Ultromics to Competitors

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Aidoc operates as a healthcare technology company providing a care coordination platform. It offers a wide range of solutions such as real-time notifications for neuro care, automatic analysis of imaging to identify abnormalities, and more. It was founded in 2016 and is based in Tel Aviv, Israel.

Kheiron Medical Technologies

Kheiron Medical Technologies operates as a healthcare software company. It helps cancer patients by analyzing medical images and helps to enable the automation of radiology reporting tasks. It was formerly known as Maesterai. The company was founded in 2016 and is based in London, United Kingdom. Logo provides an artificial intelligence (AI) based system to identify abnormalities in head computed tomography (CT) scans. It leverages deep learning to diagnose diseases from radiology and pathology imaging and create personalized cancer treatment plans from psychopathology imaging and genome sequences. The company was founded in 2016 and is based in Mumbai, India.

VoxelCloud Logo

VoxelCloud utilizes artificial intelligence and cloud-computing technologies to provide automated medical image analysis and clinical decision support for medical practitioners. VoxelCloud aims to provide accurate, accessible, and personalized medical image analysis by building medical image knowledge graph based on medical semantics and multiple artificial intelligence models.

Enlitic Logo

Enlitic is a company that focuses on healthcare data management, specifically within the domain of radiology. The company offers solutions that standardize, protect, integrate, and analyze medical imaging data, using artificial intelligence to improve clinical workflows and create a real-world evidence medical image database. Enlitic primarily serves the healthcare industry, with a particular focus on radiologists, PACS administrators, hospital executives, and researchers. It was founded in 2014 and is based in San Francisco, California.


12 SIGMA is a biotechnology company that integrates artificial intelligence and deep learning into modern medical image diagnosis and medical data analysis. 12SIGMA is based in Beijing and San Diego.


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