Healthcare data is growing exponentially. The volume and complexity of diagnostic imaging has increased significantly and continues to grow. New sources of large, complex data such as genome sequencing, biosensors and connected devices are being adopted rapidly. Doctors increasingly rely on machine learning to help drive the right decision and actions to improve patient outcomes.
images produced by a single MRI scan
million terabytes of digital imaging data
annual losses due to sub-optimal radiology processes
genomes sequenced by 2017, up from 200,000 in 2014
of physician time spent on face-to-face patient care
Our artificially intelligent radiology solution can diagnose disease from CT Scans, MRIs and X-rays as well as outline and quantify regions of interest such as tumors or lung disease patterns.
At Qure.ai our mission is to make healthcare affordable and accessible using the power of artificial intelligence. We build deep learning solutions that aid physicians with routine diagnosis and treatment, allowing them to spend more time with patients.
Determining the volume, location and boundaries of a brain tumor is critical to planning treatment. Brain tumors are complex structures, often containing distinct areas of higher activity and growth. High-grade tumors may have a necrotic core and be surrounded by edema. Capturing all this information on MRI requires the use of 4 different scanning modalities, resulting in a series containing around 600 images.
Using deep learning, we are able to precisely quantify the volume of each tumor and its zones, within seconds. Outlining the tumor pixel-by-pixel enables detection of even the smallest changes in volume. This means that patient progress can be monitored closely. By combining conventional MRI with perfusion imaging techniques that detect cerebral blood flow and volume, we are also developing a solution that can detect tumor grade.
MRI scans of joints are used to evaluate the extent of damage caused by injury or arthritis. An accurate, quantitative measure of cartilage is necessary for early diagnosis of pathological changes, and evaluation of patient progress. With the availability of stem-cell transplants for cartilage regeneration and custom-designed prosthetic joint components, cartilage measurements are also useful when planning joint surgery.
Through deep learning, we are able to detect the contours of healthy and damaged joint cartilage with an accuracy that far outperforms current methods.
Tissue biopsies are invaluable in diagnosing cancer. Biopsies are sliced into thin sections, and placed on slides for pathologists to view. Pathologists examine these slides to determine if the tumor is malignant, what the tumor grade is, and what treatment it may respond to. Each biopsy slide contains gigabytes worth of data in a 2cm square section of tissue. Performing a thorough and accurate screening requires identifying regions of interest, and examining them thoroughly.
To conclusively exclude cancer, every millimeter of the slide needs to be viewed at high magnification, amounting to 200-300 views per slide. AI can scan these images and spot regions of interest several times faster than a human pathologist. Our deep learning algorithms are trained to identify and mark out cell boundaries, and to identify features of malignant cells and tissues. Research has shown that when pathologists are aided by AI, the results are more accurate than human pathologists alone.
Interstitial lung diseases are a group of pulmonary diseases diagnosed with the help of a high-resolution CT scan. The diseases within the group are associated with vastly different modes of treatment and prognostic implications. However, they can be tricky to diagnose and physicians do not always agree on diagnosis. Studies comparing the opinions of highly trained thoracic disease specialists show surprisingly low levels of agreement on the diagnosis of certain interstitial lung diseases.
We are building deep learning algorithms that provide an objective assessment of the presence or absence of a disease pattern and its precise extent. By training with data annotated by a panel of clinicians, we can use combined expertise to provide a consensus.
Our artificially intelligent digital pathology solution can distinguish malignant from benign biopsies, and grade a variety of tumor types.
Deep learning is a form of artificial intelligence that has completely transformed image recognition and language processing. Using multilayered neural networks, we are able to train machines to interpret images and speech as humans do. The more data these networks are exposed to, the more accurately they can make a diagnosis.
Applying deep learning to medical data comes with it’s own set of challenges. Datasets are of limited size. Understanding and annotating medical images requires highly trained experts. We’ve developed methods that adapt to these challenges, and perform with excellent accuracy.
RepositoryWe believe that science progresses faster with collaborative effort, and that the best solutions are not developed in isolation. In this spirit, we open-source our research where possible.
We are a team of computer scientists, medical practitioners and bioinformaticians. We apply the latest deep learning research to healthcare questions, and develop innovative solutions that will revolutionize the way patients are diagnosed and treated.
We are looking for talented individuals to join the Qure team in India and the San Francisco Bay Area. If you want to be part of this forward-thinking company on the forefront of deep learning, apply now at email@example.com
Partnering with clinicians helps us identify the most relevant problems, and create real-world solutions. Much of our research is done in collaboration with hospitals, universities and research institutions. If you are a medical institution and would like to partner with us on a deep learning solution, please reach out.