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Accrad is unlocking the healing secrets of images, ensuring one chest X-ray at a time

Conceptual art copyrighted to HiEquity.ai

Accrad, a groundbreaking company founded on February 1, 2020, in Cape Town, South Africa, is at the forefront of leveraging AI in X-ray image analysis to address critical global health challenges. Led by visionary founders Shahram Rezasade, Thabo Koee, and Moloti Tebogo Nakampe, Accrad is dedicated to revolutionizing radiological diagnostics and enhancing healthcare accessibility.

The company’s mission is to tackle pressing global health issues, particularly in the diagnosis of diseases like tuberculosis (TB), lung cancer, and COVID-19. Accrad’s innovative approach is very important in our present-day world considering the alarming statistics provided by the World Health Organization, which reported 1.3 million TB-related deaths in 2022, with TB ranking as the second leading infectious killer worldwide, surpassed only by COVID-19. Research by Muayad Albadrani highlights that new TB cases contribute to approximately 122 million DALYs, of which 58 million are due to post-TB sequelae. Notably, TB incidence rates are significantly higher in regions like Africa and Southeast Asia, largely influenced by challenges such as limited healthcare access and socio-economic factors.

Accrad’s commitment to improving global health is further underscored by its recognition of the profound economic impact of these diseases. The worldwide economic implications of TB, encompassing treatment expenses and productivity losses, are estimated to reach billions of dollars annually.

Pain point addressed

Accrad primarily addresses the diagnostic challenges in the African healthcare sector, where there is a significant scarcity of radiological services and expertise. The region’s specific issues include high prevalence rates of diseases like tuberculosis, lung cancer, and COVID-19, compounded by socio-economic constraints and a lack of medical infrastructure. The specific pain point in the healthcare value chain addressed by Accrad lies in the radiological diagnosis, particularly the interpretation of chest X-rays. This stage is critical for the early detection of thoracic diseases, which is a common challenge in African healthcare. Accrad’s AI software, CheXRad, is designed to aid in this diagnostic phase, improving accuracy and reducing time. Accrad’s AI assists African radiologists, burdened by high workloads, to improve diagnostic accuracy. It benefits hospitals and patients, especially in underserved areas, by enhancing disease detection and management in healthcare systems.

Type of solution

Accrad provides a digital solution in the form of sophisticated AI-based software for X-ray image analysis. Specifically, their product, CheXRad, is a deep learning algorithm designed for the interpretation of chest X-rays and CT images. This software solution does not include hardware components; instead, it integrates with existing radiological equipment to enhance diagnostic capabilities. CheXRad’s AI and machine learning technology enable the concurrent diagnosis of multiple pathologies, such as COVID-19, tuberculosis, and lung cancer, thereby improving the accuracy and efficiency of radiological diagnostics in healthcare settings.

Source: http://accrad.com/

Type of input data leveraged

  • Accrad’s software solution, CheXRad, leverages chest X-ray and computed tomography (CT) images as its primary input data. These radiological images are the basis for the AI algorithm to analyze and diagnose various thoracic diseases. The software uses these medical images to identify and predict the presence of multiple pathologies, including COVID-19, tuberculosis, lung cancer, and other clinically significant diseases. The AI algorithm is trained on large datasets of these radiological images to enhance its accuracy and reliability in disease detection and diagnosis.

Key technology involved

  • Deep learning algorithms: CheXRad utilizes deep learning, a subset of machine learning, to analyze chest X-ray and CT images. This involves using convolutional neural networks (CNNs), which are highly effective in processing and interpreting visual data like medical images.
  • Convolutional neural networks (CNNs): These are specialized deep learning algorithms designed to recognize patterns and features in images. For Accrad’s technology, CNNs are trained to identify signs of diseases such as tuberculosis, lung cancer, and COVID-19 in radiological images.
  • Computer vision: This technology enables the software to interpret and analyze medical imaging data. Computer vision in CheXRad is used to detect abnormalities and specific disease markers in chest X-rays and CT scans.

Key applications of solution

  • Disease detection and diagnosis
    • Identifies various thoracic diseases, including tuberculosis, lung cancer, and COVID-19, in chest X-rays and CT scans. It extracts features, recognizes patterns, and achieves high accuracy, providing insurers with a cost-effective tool for precise disease diagnosis and reduced healthcare costs.
  • Enhancing diagnostic accuracy and speed
    • Minimizes human error and rapidly processes radiological images, making it ideal for large-scale screenings.
  • Decision support for healthcare professionals
    • Provides radiologist assistance and guidance for general practitioners and serves as an educational tool.
  • Patient monitoring and management
    • Tracks disease progression and evaluates treatment responses, particularly useful for chronic conditions.

Implications for key stakeholders

  • Radiologists: Beyond just aiding in diagnosis, CheXRad can restructure the workflow, enabling radiologists to focus on complex cases while AI handles routine screenings. In a hospital with high patient inflow, CheXRad could pre-screen X-rays, flagging critical cases for immediate attention, thus prioritizing urgent cases and managing workload more effectively.
  • General healthcare practitioners: For doctors in remote areas without regular access to radiologists, CheXRad provides a level of diagnostic support previously unavailable. A general practitioner in a rural clinic uses CheXRad to confirm a suspected case of tuberculosis, facilitating faster treatment initiation.
  • Patients: More accurate diagnostics lead to more tailored treatment plans. A patient with an early-stage lung anomaly detected by CheXRad receives a targeted treatment plan, improving their prognosis.
  • Insurers: They stand to benefit significantly from Accrad’s technology through potential cost reductions resulting from enhanced diagnostic accuracy.
  • Regulatory bodies: Regulators will need to consider how AI tools like CheXRad fit into existing medical practice regulations. Health regulatory agencies evaluate CheXRad’s diagnostic accuracy and privacy compliance before approving its widespread use.

Current impact

  • Number of X-ray scans collected in Asia, the EU, and the US (116,756): This signifies the global reach of Accrad’s CheXRad software, used for analyzing X-ray scans from diverse regions to ensure accurate diagnoses.
  • Number of COVID-19 CT scans (349): Reflects the software’s role in diagnosing COVID-19-related lung abnormalities, contributing to managing the pandemic.
  • Machine learning models (15): highlight the software’s sophistication with specialized algorithms for diagnosing various thoracic diseases.
  • Images processed per second (4.7): Demonstrates the software’s efficiency in quickly analyzing radiological images, benefiting patient care and radiology workflows.

Potential future impact

  • With the increasing focus on population health and preventive care, there’s a growing need for advanced analytics tools. These tools could assess regional health trends, predict disease outbreaks, and optimize resource allocation.
  • Accrad’s extensive dataset of radiological images, combined with its AI capabilities, could be used to develop predictive models for regional disease patterns. By partnering with public health authorities and epidemiologists, Accrad could extend its services into population health analytics. This could enable early detection of disease hotspots and support proactive healthcare interventions, ultimately improving public health on a broader scale.
  • Leveraging its machine learning models and image analysis expertise, Accrad could explore the realm of personalized medicine. By integrating its radiological insights with genetic, clinical, and lifestyle data, Accrad could assist in tailoring treatment plans for patients. This could be especially impactful in cancer treatment, where the choice of therapy often depends on precise disease characterization.

Business model

Accrad primarily targets business-to-business (B2B) clients, such as hospitals, clinics, and healthcare systems. Accrad’s subscribers are healthcare institutions, including hospitals, clinics, diagnostic centers, and research institutions, benefiting from enhanced radiological diagnostics.

Advantages of the business model

  • Enhanced diagnostic accuracy: CheXRad improves diagnostic accuracy for healthcare providers, reducing misdiagnoses and enhancing patient care.
  • Efficiency: Rapid image analysis reduces diagnosis time, enabling healthcare providers to serve more patients effectively.
  • Reduced workload: Radiologists and healthcare professionals benefit from a reduced routine workload, allowing them to focus on complex cases.
  • Faster diagnoses: Patients receive faster and more accurate diagnoses, leading to timely treatments and better health outcomes.
  • Improved access: CheXRad extends access to high-quality radiological services, promoting health equity.

Funding and key stakeholders

Accrad presently finds itself in the “unfunded” stage, signifying its active pursuit of financial backing and investment to advance and expand its groundbreaking AI-driven radiological diagnostic solution, CheXRad. Currently, Accrad has not secured any key investor.

Competitive differentiators

Accrad’s competitive differentiator lies in its ability to deliver hyper-localized diagnostic insights, enhancing patient care and resource allocation. By fine-tuning its AI algorithms to consider region-specific disease prevalence and demographics, Accrad ensures tailored diagnostic accuracy, improving healthcare outcomes in diverse populations. This nuanced approach sets Accrad apart in the field of radiological diagnostics, where precision matters most.

Relevant regulatory and compliance requirements

A solution like Accrad’s, which deals with sensitive medical data and radiological diagnostics, would necessitate continuous upkeep and maintenance of compliance status with pertinent regulations such as those enforced by the FDA, HIPAA, and other relevant healthcare authorities. Ensuring compliance with these regulations is vital for safeguarding patient data, preserving privacy, and upholding the quality of healthcare services.

Areas for continuous improvement

  • Accrad’s continuous improvement could focus on further enhancing the interpretability and transparency of its AI algorithms. By making the decision-making process of its AI models more understandable to healthcare professionals, Accrad can build greater trust in its technology, ultimately improving adoption and collaboration with radiologists and clinicians.




Disclaimer: Please note that the opinions, content, and analysis in my posts are entirely my own and do not reflect the views of any current or past employers or institutional affiliations. These posts, based solely on publicly available information, are for informational purposes and should not be taken as professional advice. All insights and conclusions are my viewpoints and should not be considered representative of any organizations I am or have been associated with. This content is not endorsed by, nor does it represent the stance of any affiliated entity.


Hiequity Team

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