The problem we solve: Today, ambulances lack an early recognition device that is connected to hospitals that EMTs can use to detect traumatic brain injuries. Patients instead must wait on average  minutes until they get to a hospital, at which time a doctor runs through a triage checklist to determine if there might be a TBI. The potential for mis-diagnosis is high given non-presenting symptoms and doctor expertise. Cost concerns also come into play as brain scans are exorbitantly expensive. At this point if diagnosed with a TBI, the patient is taken for a CT scan, where they may have to wait further depending on availability and order of criticality compared to other patients. The compounding effect of lost time and diagnosis error contributes to the over 53,000deaths annually in the US from TBIs and the more than 5.3M(Source: CDC) Americans currently living with long-term disabilities directly attributable to TBIs.
About our solution: PONS is a patent-pending machine learning approach for the automatic identification and risk categorization of traumatic brain injuries and spine injuries by using ultrasound (US) scans to detect anatomical brain features. Delivered as an easy-to-use and mobile prediagnostic tool, PONS enables any qualified medical professionals in the field in seconds to assess the probability of a TBI, saving critical time that could mean the difference between a patient's full recovery or unnecessary deathProgress to date:
We have expertise in the area of deep learning, algorithm development, and application of ultrasound imaging for various surgical and non-surgical procedures. We have identified a unique need that is not addressed currently: A portable, noninvasive, data-driven imaging solution for on-site TBI assessment.
The research of PONS has started 3 years ago, we have done several clinical trials as of today. Because of COVID, the clinical trials have stopped. Our road map is to get FDA approval by the end of 2021 and lunch the product.
Data is the new oil in AI-based health care technology. Currently, we are in the process of collected ultrasound data from healthy as well as individuals who are diagnosed with TBI. Once the data collection is finalized we will train our previously developed deep learning methods on the collected data.
The main objective of PONS is validation, and deployment of new computational tools, based on deep learning, for processing multi-feature ultrasound data to derive models that yield individual-level accurate risk assessment and TBI diagnosis and monitoring. Our solution is to operate on a standard point of care ultrasound probe. Therefore, our solution provides a clear path for fast adaptation. Due to its compact nature, our proposed solution also lends itself to repeated bedside imaging studies for monitoring TBI progression which is crucial to adjust the treatment plan. The use of point of care ultrasound for TBI patient risk assessment and treatment response monitoring has not been investigated so far. We will collect ocular ultrasound and transcranial ultrasound data. B-mode (grayscale intensity-based) ultrasound data, radio frequency (RF) raw ultrasound signal data, and local phase tissue signatures will be treated as multi-feature data. Such an integrative data collection using ultrasound in the context of TBI diagnosis, risk assessment, and monitoring is currently not available.
Creator: soner hacihaliloglu
Location: New Jersey
Bio: Soner Hacihaliloglu started his professional career as Project Manager at Siemens Ares-ecount Technologies. After working 3 years in Building Technologies he was appointed as the MENA Region Business Development manager at E-on Ista Holding. During the time he served as a Business development manager he was responsible for the operations in 5 different countries on 3 continents. He set up operations from 0 to 50m Euro revenue in 2 years. Soner has been chosen as one of the best Innovators under 40 and the Best young Energy Professional in the MENA region in 2017 and 2018. As of 2020, Soner is the CEO of PONS Technologies, a company that has been awarded by the UN, Europe Union, Sustainable Management Assotioains as one of the best innovations Soner has a Computer Science Degree from California University Newport and an MBA degree from Erasmus University Rotterdam School of Management
Title: CEO / Co-founder
Ass.Prof.Dr. Ilker Haci
CTO, PhD, Prof
Biography: Ass. Prof. Dr. Ilker Hacihaliloglu is the director of the Computer-Assisted Surgery and Therapy Laboratory (CompAST) at Rutgers University’s multidisciplinary research laboratory focusing on developing non-robotic surgical systems. He has over 15 years of experience in computer-aided surgery and early diagnostics systems in the brain, cancer treatment fields.
Advanced Degree(s): PhD, Prof
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