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EMPOWERING HEALTHCARE DIAGNOSIS WITH ARTIFICIAL INTELLIGENCE
OUR PATENT PENDING* TECHNOLOGY
01 / FAST
Chest X-rays are currently the best available method for diagnosing different lung associated diseases like hernia, pneumonia, fibrosis, edema, emphysema, cardiomegaly, pleural thickening, consolidation, pneumothorax, mass, nodule, atelectasis, effusion and infiltrations. Our application can detect diagnosis of each of these conditions faster than an average processing time from a radiological laboratory.
02 / SECURE
The users private radiological data is analyzed on a secure cloud platform.
03 / EASY
Our application is easy to use and can be used easily with no prior experience.
Images
Tested
112,000
Turnaround
Time
< 1 Minute Per Image
Reporting Efficiency
90-95%
Product
About
MEET ChestAi
OUR STORY
ChestAi was started with computer scientists and geneticists at Yale university with a common motivation of providing a free and open source platform for rapid and robust diagnosis of pathologies identified by radiological examinations.
OUR VISION
Chest X-ray exam is one of the most frequent and cost-effective medical imaging examinations. However clinical diagnosis of chest X-ray can be challenging, and sometimes believed to be harder than diagnosis via chest CT imaging. Even some promising work have been reported in the past to achieve a clinically relevant computer-aided detection and diagnosis (CAD) in real world medical sites on all data settings of chest X-rays is still very difficult. Our vision is to decrease the diagnosis burden with improving diagnosis accuracies.
OUR TECHNOLOGY
With approximately 2 billion procedures per year, chest X-rays are the most common imaging examination tool used in practice, critical for screening, diagnosis, and management of lung diseases. However, an estimated two thirds of the global population lack access to radiology diagnostics. With automation at the level of experts, we hope that this technology can improve healthcare delivery and increase access to medical imaging expertise in parts of the world where access to skilled radiologists is limited.
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Latest Computer Vision & Biomarker Discovery R&D Publications
1. Common cancer biomarkers of breast and ovarian types identified through artificial intelligence, Shrikant Pawar, Tuck Onn Liew, Aditya Stanam, Chandrajit Lahiri, Wileys: Chemical Biology and Drug Design, 96(3):995-1004. doi: 10.1111/cbdd.13672. Epub 2020 May 15. PMID: 32410355. IF=2.9
2. Developing a DEVS-JAVA Model to Simulate and Pre-test Changes to Emergency Care Delivery in a Safe and Efficient Manner, Shrikant Pawar and Aditya Stanam, Springer: Lecture Notes in Computer Science, vol 11466. Springer, Cham. https://doi.org/10.1007/978-3-030-17935-9_1.
3. Scalable, reliable and robust data mining infrastructures, Shrikant Pawar and Aditya Stanam, IEEE Fourth World Conference on Smart Trends in Systems, Security and Sustainability, 2020, pp. 123-125, doi: 10.1109/WorldS450073.2020.9210388.
4. A Six-Gene-Based Prognostic Model Predicts Survival in Head and Neck Squamous Cell Carcinoma Patients, Shrikant Pawar and Aditya Stanam, Springer: Journal of Maxillofacial and Oral Surgery, 2019 Jun;18(2):320-327. doi: 10.1007/s12663-019-01187-z. Epub 2019 Jan 24. PMID: 30996559; PMCID: PMC6441444. IF=1.0
5. Clustering Reveals Common Check-Point and Growth Factor Receptor Genes Expressed in Six Different Cancer Types, Shrikant Pawar and Aditya Stanam, Springer: Lecture Notes in Computer Science, vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_52.
6. Linear regression model for prediction of multi-dimensional time-point forecasting data, Shrikant Pawar and Aditya Stanam, ITISE, I.S.B.N: 978-84-17970-78-9, Legal Deposit: Gr 1209-2019
7. Stochastic dimension reduction techniques for time-point forecasting data, Shrikant Pawar and Aditya Stanam, ITISE, I.S.B.N: 978-84-17970-78-9, Legal Deposit: Gr 1209-2019
8. *System and method to detect lung disorders in chest x-ray image: Patent Pending, ID-TEMP/E-1/26/2021-DEL & USPTO trademark application for ‘ChestAi’ (#88930063)
9. Evaluating the computing efficiencies (specificity and sensitivity) of graphics processing unit (GPU)-accelerated DNA sequence alignment tools against central processing unit (CPU) alignment tool, Shrikant Pawar, Aditya Stanam and Ying Zhu, Journal of Bioinformatics and Sequence Analysis, 9(2), 10-14.
10. Predicting the prognosis for cancer patients with interleukins gene expression level, Aditya Stanam, and Shrikant Pawar, AACR: Cancer Research, Print ISSN 0008-5472, Online ISSN 1538-7445, DOI: https://doi.org/10.1158/1538-7445.AM2019-4247. IF=13.3
11. Software effort prediction with algorithm-based frameworks, Shrikant Pawar and Aditya Stanam, International Journal of Engineering and Computer Science, 7(09), 24206–24213. Retrieved from http://www.ijecs.in/index.php/ijecs/article/view/4174.
12. Machine learning for identification and characterization of molecular gene signatures in progression of benign tumors, Shrikant Pawar, Aditya Stanam, and Rushikesh Chopade, ACM Proceedings, 1-3, https://doi.org/10.1145/3469213.3469214
13. Techniques of time series modeling in complex systems, Shrikant Pawar and Aditya Stanam, Springer Lecture Notes in Networks and Systems, 978-981-16-2377-6, DOI: 10.1007/978-981-16-2377-6
14. Single shot detector application for image disease localization, Shrikant Pawar, Aditya Stanam, Rushikesh Chopade, bioRxiv 2021.09.21.461307; DOI: https://doi.org/10.1101/2021.09.21.461307
15. Cyclical Learning Rates (CLR’s) for Improving Training Accuracies and Lowering Computational Cost. Rushikesh Chopade, Aditya Stanam, Anand Narayanan, Shrikant Pawar. Research Square. DOI:10.21203/rs.3.rs-1129014/v1
16. Neural Networks for Predicting Severity of Ovarian Carcinomas, Rushikesh Chopade, Aditya Stanam, Shrikant Pawar. Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 578. Springer, Singapore. https://doi.org/10.1007/978-981-19-7660-5_7
17. K-fold Semi-supervised Self-learning Technique for Image Disease Localization, Rushikesh Chopade, Patil Abhijit, Stanam, Shrikant Pawar. Springer Advances in Intelligent Systems and Computing Print ISBN 978-981-19-9818-8. DOI: https://doi.org/10.1007/978-981-19-9819-5_49
18. Cyclical Learning Rates (CLR’s) for Improving Training Accuracies and Lowering Computational Cost. Rushikesh Chopade, Aditya Stanam, Anand Narayanan, Shrikant Pawar. Springer Lecture Notes in Computer Science, 13920. DOI: https://doi.org/10.1007/978-3-031-34960-7_23
19. Stroke Risk Stratification Using Neural Networks, Publication Springer Lecture Notes in Networks and Systems, vol 812. DOI: https://doi.org/10.1007/978-981-99-8031-4_3
1. 4th International Conference on Applied Mathematics and Simulation 2021. Machine learning application in genomics.
2. 2nd International Conference on Artificial Intelligence and Information Systems (ICAIIS 2021). Artificial intelligence and cancer biomarker discovery.
3. 8th International Work-Conference on Bioinformatics and Biomedical Engineering, Granada, Spain, 2020. Clustering reveals common check-point and growth factor receptor genes expressed in six different cancer types.
4. 4th Smart Trends in Systems, Security and Sustainability Conference, London, UK, 2020. Scalable, reliable and robust data mining infrastructures.
5. American Association for Cancer Research, Atlanta, GA, March 2019. Predicting the prognosis for cancer patients with interleukins gene expression level. Poster
6. 6th International Congress on Information and Communication Technology, London, UK, 2021. Techniques of time series modeling in complex systems.
7. Radiological Society of North America (RSNA) Annual Conference, Chicago, USA, 2021.
8. HP HBCU Technology Conference 2023
8. 6th World Conference on Smart Trends in Systems, Security, and Sustainability, London, UK, 2022. Neural Networks for predicting severity of ovarian carcinoma. Link
9. 10th International Work-Conference on Bioinformatics and Biomedical Engineering, Gran Canaria, Spain, 2023. Cyclical Learning Rates (CLR’s) for Improving Training Accuracies and Lowering Computational Cost. Poster Presentation Link
10. 6th International Conference on Computational Vision and Bio Inspired Computing ICCVBIC 2022 Link
11.Congress on Smart Computing Technologies (CSCT 2023), SAU Center for Research and Innovative Learning (SCRIL), Evaluating Differences in Small Object Localization using Semantic Segmentation & Single Shot Detector (SSD) Bounding Box Algorithm Link
12. 5th National Big Data Health Science Conference (2024), University of South Carolina, Columbia, Cyclical Learning Rates (CLR’S) for Improving Training Accuracies and Lowering Computational Cost, Rushikesh Chopade, Aditya Stanam, Anand Narayanan & Shrikant Pawar Link Poster
13. 9th International Congress on Information and Communication Technology (ICICT 2024) London, United Kingdom, Addressing Class Imbalance Problem in Semantic Segmentation using Binary Focal Loss, Rushikesh Chopade, Aditya Stanam, & Shrikant Pawar
14. 2024 University of South Carolina 2nd Annual Research Core Fair, Columbia, Cyclical Learning Rates (CLR’S) for Improving Training Accuracies and Lowering Computational Cost, Rushikesh Chopade, Aditya Stanam, Anand Narayanan & Shrikant Pawar
15. 7th World Conference on Smart Trends in Systems, Security, and Sustainability (WorldS4 2023), Stroke risk stratification using neural networks, Shrikant Pawar
1. Kickstarting health care innovation with artificial intelligence. Tsai Center for Innovative Thinking at Yale (Tsai CITY) Editors: ReCore team, Andrew Nguyen, & David Hou.
2. How NGS and big data can be instrumental in tackling genetic disorders. SelectScience, Editor: Charlotte Carter
3. ChestAi with Yale MD/MBA candidate Anna Zhao selected as semi-finalists for New Haven chapter of Nucleate activator program
4. We thank our past and current interns Dr. Rayudu, Mr. S Kumar(Git), Mr. R Chopade(Git), Mr. Samarth, Mr. Rahul(Git), Mr. Rohan Sharma (Git), Ms. V Bhaskara from Indian Institute of Technology (IIT), Kharagpur and Guwahati, India for continued efforts. We hope our internship program will benefit their future aspirations. Students interested in this program can contact our office for more information.
5. ChestAi product gets listed on AL4HLTH from United Nations Office for Project Services (UNOPS) StopTB Partnership & Foundation for Innovative New Diagnostics (FIND)
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