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A proof of concept demonstrated reliable detection of ovarian cancer subtypes with reported 95% accuracy and provides subtype percentages per tile to support precise, detailed diagnostic 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low cost medical device framework for cancer diagnosis  \nSamaira Mehta1,2􀀍  \nCancer remains a major global health challenge, with significant disparities in access to advanced diagnostic and prognostic technologies, especially in resource-constrained settings. Existing medical treatments and devices for cancer diagnosis are often prohibitively expensive, limiting their reach and impact. Pathologists’ scarcity exacerbates cancer diagnosis accuracy, elevating mortality risks. To address these critical issues, this study presents OVision-a low cost, deep learning-powered framework developed to assist in histopathological diagnosis. The key objective is to leverage the portable, low-power computing Raspberry Pi. By designing standalone devices that eliminate the need for internet connectivity and high-end infrastructure, we can dramatically reduce costs while maintaining accuracy. As a proof of concept, the study demonstrated the viability of this framework through a compact, self-contained device capable of accurately detecting ovarian cancer subtypes with 95% accuracy, on par with traditional methods, while costing a small fraction of the price. This portable, off-grid solution has immense potential to improve access to precision cancer diagnostics, especially in underserved regions of the world that lack the resources to deploy expensive, infrastructure-heavy medical technologies. In addition, by classifying each tile, the tool can provide percentages of each histologic subtype detected within the slide. This capability enhances the diagnostic precision, offering a detailed overview of the heterogeneity within each tissue sample, helps in understanding the complexity of histologic subtypes and tailoring personalized treatment plans. In conclusion, this work proposes a transformative model for developing affordable, accessible medical devices that can bring advanced healthcare benefits to all, laying the foundation for a more equitable, inclusive future of precision medicine.  \nKeywords Cancer, Diagnostics, Deep learning, Histopathology, Raspberry Pi, Precision medicine, Ovarian cancer  \nCancer remains a one of the biggest global health challenges, claiming an estimated 10 million lives in 2020 alone1. Despite significant advancements in diagnostics and treatment, access to these vital resources remains severely constrained2–4 in many regions, exacerbating the burden of the disease. Compounding this issue is the reliance on pathologists whose expertise is pivotal in accurate cancer diagnosis; however, a shortage5 of these specialists and slow turnround time of slide evaluation can delay treatment and/or negatively impact patient well-being.  \nRecognizing these challenges, research teams and commercial entities are leveraging AI to develop tools aimed at assisting pathologists6. While these innovations are promising, the high cost associated with such medical devices presents a barrier to widespread deployment, particularly in resource-limited settings.  \nOne malignancy that embodies these challenges is ovarian cancer. With a 5-year survival rate7 of just 49.4%, ovarian cancer exemplifies the pressing need for accurate and timely detection to improve diagnosis. Ovarian cancer is characterized by significant histological heterogeneity, which poses challenges for accurate diagnosis and treatment. This heterogeneity is not only observed between patients but also within individual tumors. The complex nature of ovarian cancer histology often requires pathologists to review multiple slides from different regions of a tumor to make an accurate diagnosis. Given this inherent complexity, the classification of ovarian cancer subtypes from histopathological images has seen significant progress due to advancements in machine learning and deep learning techniques. Initial efforts utilized traditional image processing and clas","cbCaisvqJVM95ZgA","https://ap.wps.com/l/cbCaisvqJVM95ZgA","pdf",3960858,16,"English","# Introduction\n## Literature review","[{\"question\":\"What problem does OVision aim to solve in cancer diagnosis?\",\"answer\":\"OVision targets the cost and accessibility gaps in advanced cancer diagnostics, especially where pathologists are scarce and slide evaluation is slow, which can reduce diagnostic accuracy and delay treatment.\"},{\"question\":\"How does OVision reduce cost and deployment barriers?\",\"answer\":\"It uses a portable Raspberry Pi-based framework designed as a standalone, off-grid device that does not require internet connectivity or high-end infrastructure.\"},{\"question\":\"What capabilities does OVision provide for ovarian cancer histopathology?\",\"answer\":\"It can detect ovarian cancer subtypes and classify each tile, producing percentages for each histologic subtype within a slide to support more detailed diagnostic precision.\"}]","OVisionA raspberry Pi powered portable low cost medical device framework for cancer diagnosis | PDF",1790187941]