Use AI to make healthcare more accessible and affordable for all
Why AI for Healthcare?
Transform Patient Care
Sgmoid uses deep learning techniques to make doctors faster and more accurate
- Medical centers are increasingly using early detection systems supported by algorithms or automated recognition of patterns in patient data.
- Dermatologist-level classification of skin cancer with deep neural networks.
We are alumni of IIT and Ivy Leagues with over decades of experience in Healthcare, Imaging and Radiology. Deep learning is a technology inspired by the workings of the human brain. Networks of artificial neurons analyze large datasets to automatically discover underlying patterns, without human intervention. Sgmoid uses deep learning networks to examine millions of images to automatically learn to identify disease. Unlike traditional Computer Aided Diagnostics (CAD), deep learning networks can scout for many diseases at once. They can also provide rich insights in areas such as early detection, treatment planning, and disease monitoring.
Areas of application
We developed an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists.
We accurately identified nerve structures in ultrasound images and thus effectively improving the efficiency of a patient’s pain management catheter.
AI will be used to "read" biomedical images more accurately than medical personnel alone — providing better early cervical cancer detection at lower cost than current methods.
All individuals with diabetes, irrespective of the type of diabetes, require regular and repetitive annual retinal screening for early detection and timely treatment of diabetic retinopathy. AI can power this!
There are many types of breast cancers, and correctly identifying each one is important to determine the proper treatment.
Invasive Ductal Carcinoma (IDC) is the most common subtype of all breast cancers. To assign an aggressiveness grade to a whole mount sample, pathologists typically focus on the regions which contain the IDC. As a result, one of the common pre-processing steps for automatic aggressiveness grading is to delineate the exact regions of IDC inside of a whole mount slide.
Watch how AI can accurately diagnose the type of breast cancer.
How can we apply deep learning to screening programs?
Many populations that are at risk of developing a specific disease are recommended to be regularly checked, even when seemingly healthy. Nearly 70% of U.S. women over 40 receive biannual breast cancer tests. Other screening programs exist for prostate, cervical, and lung cancer.
Our screening solutions quickly analyze cases to discover and highlight suspicious findings, helping your doctors work efficiently when dealing with large patient loads.
In benchmarking tests against the publicly available LIDC lung cancer screening dataset, Sgmoid’s technology can judge the malignancy of nodules in chest CT images 50% more accurately than an expert panel of radiologists.
Decrease false positive rates significantly!
Detect tiny fractures as small as 0.01% of an X-ray image!
Pay as you go
- Upto 20 images/month.
- Limited to Bronchitis, Cervical, Breast Histopathology
- 500 MB Test Data Size Limit
- Technical Support 1 day, Email
- Reach out to us in case you want custom Deep Learning Models for your enterprise.
- 1 Terabyte Size Limit
- Technical Support 8 hours, Phone
Meet our world class team
Jayanth RasamsettiChief Executive Officer
Pradeep MeegadaVice President
Dr Srikanth SundararajanAdvisor
Naveen VunnamVice President
Teja UNVChief Technology Officer
TeslaChief Happiness Officer
Question? Comment? Feedback? Drop us a line. We'd love to hear from you!
146W 57th Street
New York, NY 10019
Call us +1 (917) 740 1966
Open 9am-6pm Eastern Time (Monday - Friday)