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Beyond the biopsy:
A journey towards painless cancer detection.
A Deep Learning Approach to the traditionally painful and scary medical procedure.
tl;dr
Our core mission is to drastically reduce the reliance on complex, invasive medical biopsies.
Traditional biopsy procedures are often inefficient, fraught with complications, and cause significant delays—taking an average of 7–14 days for final results. This waiting period not only increases patient anxiety but can also lead to delayed treatment and potential pathological errors.
We are revolutionizing cancer detection by applying our proprietary Deep Learning Algorithm (CanScan). This advanced technology analyzes radiological features from CT, MRI, and PET scans to correlate them with pathological outcomes, effectively serving as a non-invasive diagnostic platform.
We are incubated at the Council of Scientific and Industrial Research - National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), leveraging their world-class infrastructure for our research.
Furthermore, our development trajectory is accelerated by the Kerala Startup Mission (KSUM), whose support through grants & funding has been instrumental in our growth. We also work in close partnership with the Regional Cancer Centre, Thiruvananthapuram, ensuring our solutions are grounded in clinical reality.
Foreword
The project was born from a deeply personal experience that highlighted the limitations of traditional biopsy procedures. Witnessing firsthand the anxiety and discomfort our friend endured during a biopsy, and understanding the potential consequences of delayed diagnosis which was fueled by the fear of actually undergoing the procedure, that eventually led to the progression of the cancer to its last stage, fueled our determination to find a better way.
Introduction
While tissue biopsies have long been the undisputed gold standard for cancer diagnosis, they come with a hidden cost: agonizing delays and inherent physical risks. Currently, patients must endure invasive procedures that carry dangers of infection and bleeding, only to face a nerve-wracking 7-to-14-day wait for final results. This inefficiency creates a critical bottleneck in treatment initiation and is often plagued by "necrotic sampling errors," where the biopsy needle misses the active tumor entirely, leading to inconclusive results and repeat surgeries. The medical system is essentially clogged with false alarms, subjecting patients with benign nodules to unnecessary anxiety and intervention.
At Cytoscan Intelligence, we are addressing these limitations by shifting the diagnostic paradigm from invasive extraction to intelligent computation. We are developing a robust Deep Learning architecture designed to do what the human eye cannot: correlate complex radiological features from CT, MRI, and PET scans directly with pathological outcomes. Unlike generic computer vision tools that simply look for shapes, our model integrates multi-modal imaging history to provide a precise Risk Stratification Score (Benign vs. Malignant) without a single incision.
The impact of this shift is transformative. By moving to a non-invasive diagnostic platform, we reduce the diagnostic inference time from weeks to minutes. This speed not only eliminates the backlog caused by lab unavailability but also allows oncologists to prioritize high-risk patients instantly. Ultimately, our goal is to defer 30–50% of unnecessary biopsies, ensuring that diagnostic precision no longer comes at the expense of patient comfort or time.
What are biopsies?
Biopsies have long been the standard for diagnosing tumors, requiring invasive tissue extraction and microscopic analysis, yet they come with significant drawbacks. This surgical process is not only a source of patient fear and discomfort but is also notoriously slow, often taking 7–14 days to deliver final results due to lab processing and potential complications.
At Cytoscan, our mission is to drastically reduce the need for these complex, invasive procedures. This rapid turnaround ensures that high-risk patients are prioritized immediately, effectively accelerating the journey from detection to life-saving treatment.
How are we doing it?
We achieve this precision by leveraging our proprietary Deep Learning architecture, CanScan. Unlike generic computer vision tools that merely identify shapes, CanScan is specifically engineered to ingest complex DICOM data from MRI, CT, and PET scans and correlate radiological features directly with pathological outcomes.
This allows us to move beyond simple detection to providing a comprehensive Risk Stratification Score (Benign vs. Malignant) and surgical guidance heatmaps. This not only spares patients from the physical discomfort and financial burden of unnecessary biopsies but also ensures that critical treatment can begin immediately.
The Outcomes
At the end,
We aim to defer 30–50% of unnecessary biopsies specifically on benign nodules, sparing patients from avoidable surgeries.
We reduce overall treatment costs by eliminating the expenses associated with surgical admissions, anesthesia, and managing complications.
We replace a fear-inducing, 14-day waiting period with a stress-free diagnostic experience that delivers results in minutes, significantly improving patient comfort and prognosis
We significantly improve the overall patient experience throughout their diagnosis journey.
Conclusion
We are redefining the standard of care, moving from invasive extraction to intelligent, non-invasive analysis. We envision a future where the anxiety of a 14-day biopsy wait is replaced by the certainty of an instant diagnosis derived directly from CT, MRI, and PET scans.
The impact of this shift extends far beyond the 30–50% reduction in unnecessary biopsies we aim to achieve. By accelerating the diagnostic timeline to minutes, we empower clinicians to initiate life-saving treatments immediately, drastically improving patient prognosis and comfort. Through unwavering dedication and collaboration with multiple institutions, we are committed to building a brighter, faster, and more humane future for cancer diagnosis in India and beyond.
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