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<OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-05-20T08:39:18Z</responseDate><request verb="GetRecord" metadataPrefix="oai_dc">https://keep.lib.asu.edu/oai/request</request><GetRecord><record><header><identifier>oai:keep.lib.asu.edu:node-200830</identifier><datestamp>2026-04-23T15:58:06Z</datestamp><setSpec>oai_pmh:all</setSpec><setSpec>oai_pmh:repo_items</setSpec></header><metadata><oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><dc:identifier>200830</dc:identifier>
          <dc:identifier>https://hdl.handle.net/2286/R.2.N.200830</dc:identifier>
                  <dc:rights>http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
          <dc:rights>http://creativecommons.org/licenses/by-nc-sa/4.0</dc:rights>
                  <dc:date>2025-05</dc:date>
          <dc:date>2028-05-23T05:00:00</dc:date>
                  <dc:format>56 pages</dc:format>
                  <dc:contributor>Hanson, Joshua</dc:contributor>
          <dc:contributor>Muthuswamy, Jitendran</dc:contributor>
          <dc:contributor>McBane II, Robert</dc:contributor>
          <dc:contributor>Barrett, The Honors College</dc:contributor>
          <dc:contributor>School of Biological &amp; Health Systems Engineering</dc:contributor>
                  <dc:description>Peripheral Artery Disease (PAD) remains a widespread yet underdiagnosed vascular condition affecting millions of individuals worldwide. Existing diagnostic methods—most notably the Ankle-Brachial Index (ABI)—are hindered by interobserver variability, limited prognostic insight, and a predominantly reactive approach to care. This thesis explores how an artificial intelligence (AI)-enhanced Doppler ultrasound can address these shortcomings by increasing diagnostic precision, facilitating real-time data analysis, and empowering a preventative model of vascular medicine. 

Through a comprehensive review of current PAD prevalence, treatment costs, and clinical workflows, the paper identifies key technology gaps and unmet needs. An outline of a high-level market analysis framework underscores the device’s potential impact in diverse clinical settings, demonstrating how AI-driven Doppler technology can reduce costs, streamline workflows, and ultimately improve patient outcomes. Regulatory considerations and reimbursement opportunities are introduced to illustrate the medical device pathway, emphasizing the importance of value-based care initiatives in driving adoption.

To contextualize the commercial viability of the proposed solution, a strategic, high-level business model and go-to-market approach is discussed. This includes exploring potential distribution channels, revenue structures, and the importance of positioning the technology for scalable growth. The conclusion highlights future directions for research, continued development, and the broader goal of transitioning from reactive to preventative vascular healthcare. By merging clinical innovation with entrepreneurial strategy, this thesis aims to showcase both the urgent need and feasible pathways for revolutionizing PAD diagnostics.
</dc:description>
                  <dc:subject>Peripheral Artery Disease (PAD)</dc:subject>
          <dc:subject>Artificial Intelligence</dc:subject>
          <dc:subject>Doppler Ultrasound</dc:subject>
          <dc:subject>Preventative Medicine</dc:subject>
          <dc:subject>Ankle-Brachial Index (ABI)</dc:subject>
          <dc:subject>Value-Based Healthcare</dc:subject>
          <dc:subject>Go-to-Market Strategy</dc:subject>
          <dc:subject>Medical Device Commercialization</dc:subject>
          <dc:subject>Regulatory Pathways</dc:subject>
          <dc:subject>FDA 510(k) Clearance</dc:subject>
                  <dc:title>Revolutionizing Vascular Diagnostics: The AI-Driven Pocket Doppler Probe and Its Disruptive Approach to Preventative Vascular Medicine</dc:title></oai_dc:dc></metadata></record></GetRecord></OAI-PMH>
