Here\u2019s how these three technological innovations are converging to reshape cardiac imaging.\u00a0<\/span><\/p>\n <\/p>\n Ultra-high resolution imaging is a crucial tool that enables clinicians to discern subtle details of cardiac anatomy and pathology. Current imaging modalities, including echocardiography, cardiac magnetic resonance imaging (MRI), and computed tomography (CT), already provide considerable detail. However, being able to visualize the heart with unprecedented clarity makes it easier for clinicians to detect diseases in their earlier stages. For example, higher-resolution imaging could allow clinicians to identify atherosclerotic characteristics sooner and provide recommendations to help slow its progression. Similarly, higher resolutions could make subtle changes in conditions such as early-stage cardiomyopathy or myocarditis more readily apparent, allowing clinicians to assess and treat them more effectively.\u00a0<\/span><\/p>\n Implementing ultra-high resolution imaging requires:<\/span><\/p>\n Addressing these considerations will help clinicians unlock new diagnostic insights and make ultra-high resolution imaging a crucial component of cardiac care.<\/span><\/p>\n <\/p>\n There is an increasing availability and adoption of portable point-of-care imaging devices. Traditionally, cardiac imaging has required patients to be in dedicated imaging suites, introducing scheduling delays and logistical complexities, especially in smaller practices or for patients with limited mobility. The prevalence of compact or handheld ultrasound devices alters this paradigm, bringing diagnostic capabilities directly to patients.<\/span><\/p>\n These portable systems offer greater flexibility and convenience for rapid cardiac assessments. In situations when time is critical, such as acute heart failure, the ability to quickly visualize cardiac structures and function can lead to life-saving decisions and treatment plans. Additionally, portable devices facilitate frequent patient monitoring, providing clinicians with regular insights for implementing and adjusting treatment.<\/span><\/p>\n While image quality and appropriate training will be necessary for widespread and effective use, portable point-of-care cardiac imaging democratizes access to vital diagnostic information. It empowers clinicians to make timely decisions and enhance patient care.<\/span><\/p>\n <\/p>\n Integrating AI<\/a> into cardiac imaging workflows holds immense promise for enhancing diagnostic accuracy, improving efficiency, and personalizing patient care. Once could argue that cardiac ultrasound could be the next vital sign. AI algorithms, machine learning, and deep learning techniques enable the analysis of vast amounts of imaging data, allowing clinicians to easily and consistently identify subtle patterns and anomalies. Modern workflow solutions that support integration with automated image analysis, and robust structured reporting tools has the potential to revolutionize how clinicians interpret and utilize cardiac images.<\/span><\/p>\n The true power of unlocking cardiac imaging excellence is closely tied to the power of AI support. AI algorithms learn from data, and in medical imaging, they are trained on large datasets of cardiac images. Practices must consistently maintain high image quality to leverage robust, reliable AI diagnostic and reporting systems. Images with poor resolution, inconsistent acquisition parameters, or inadequate annotations may lead to biased or inaccurate AI models.<\/span><\/p>\n Achieving AI-driven cardiac diagnostic excellence requires a commitment to high standards throughout the medical imaging process, from image acquisition and adherence to protocols to image viewing and reporting solutions that are accessible and secure.<\/span><\/p>\n To utilize AI effectively, practices must implement:<\/span><\/p>\n Without this solid foundation in medical imaging excellence, AI’s potential to transform cardiac care<\/a> cannot be fully realized.<\/span><\/p>\n <\/p>\n These three trends are shaping the future of cardiac imaging and cardiac care. <\/span>Core Sound Imaging<\/span>, with its Studycast system, is helping clinicians and practices capitalize on these opportunities.\u00a0<\/span><\/p>\n Studycast is designed for usability and engineered for performance, providing a single, intuitive solution to manage the entire cardiology imaging workflow. Because timely reporting is a crucial component of portable point-of-care imaging, Studycast allows clinicians to:<\/span><\/p>\n With a user-friendly platform that integrates image viewing, reporting, and archiving, Studycast helps practices and clinicians streamline their workflow for better patient care outcomes.<\/span><\/p>\n <\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n <\/p>\n This article was written by Laurie Smith.<\/em><\/p>\n Laurie Smith is a principal and CRO at Core Sound Imaging\u2014makers of the Studycast System, a comprehensive imaging workflow platform. The Studycast System is a platform for medical imaging workflow that has been disrupting and streamlining medical image storage and reporting for 18 years. Studycast is connecting physicians to their images and interpretation tools from any Internet-connected device.<\/em><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" Clinicians are able to visualize the heart in unprecedented detail due to advancements in ultra-high resolution imaging. Portable point-of-care solutions are also helping to reduce barriers to timely patient diagnoses. In addition, the assistance of artificial intelligence (AI) enables clinicians to extract more in-depth insights and gather additional information from these images. Together, these advancements […]<\/p>\n","protected":false},"author":14,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_et_pb_use_builder":"on","_et_pb_old_content":" This article was published in the August 2023 issue<\/a> of Echo Magazine.<\/em><\/p> \u00a0<\/p> The transformative power of AI in the realm of medical imaging has been the subject of extensive discussion for over a decade. However, the practical implementation of AI algorithms in this field, although promising, is laden with challenges. The FDA has, to date, approved over 500 AI algorithms for medical imaging, but transforming these algorithms into affordable, usable products remains a hurdle for companies. Furthermore, the integration of these tools into the imaging workflow often introduces friction, thus inhibiting widespread adoption.<\/p> \u00a0<\/p> A paper published in Frontiers in Cardiovascular Medicine[1]<\/a> in 2019 emphasized the potential for medical imaging AI to enhance the quality, equality, and cost-effectiveness of healthcare systems. The authors also forecasted other benefits such as improved patient-physician relationships, better healthcare delivery, and increased physician job satisfaction. Yet, they also acknowledged the challenges AI implementation faces, predicting its initial application in well-circumscribed tasks, with the ultimate goal of integrating these tasks into a seamless and efficient pipeline.<\/p> \u00a0<\/p> The ideal scenario is for AI integration into the existing workflow to be seamless, leading to improved outcomes as the only noticeable difference from a consumer standpoint. To achieve this goal, it's crucial to address several key obstacles: earning the physicians' trust in AI tools, enhancing accessibility, ensuring transparency, and granting physicians the autonomy to decide whether to utilize or dismiss AI-derived information.<\/p> \u00a0<\/p> As of early 2023, cardiology-related AI algorithms rank second among imaging specialties in the number of FDA-approved AI algorithms. Many companies are developing solutions in the cardiac imaging AI space, but navigating these offerings and attaining adoption is a complex, expensive, and a time-sensitive process. When a hospital wishes to implement an AI tool, it typically entails a lengthy period of research, evaluation, purchasing and IT approvals, and resource allocation for deployment. Each selection of a new AI vendor triggers this process anew.<\/p> \u00a0<\/p> Upon selection, an AI tool often operates in isolation or remains disconnected from an institution's daily imaging workflow. Most AI algorithms for cardiac imaging today are either tied to a specific vendor or exist as separate software, which poses a challenge for busy cardiology departments that serve hundreds of patients a day. Therefore, to drive adoption, integrating the tool into the standard imaging workflow is critical.<\/p> \u00a0<\/p> Transparency is another crucial factor in building physician trust and promoting the adoption of AI tools. As Seetharam, Shrestha, and Sengupta[2]<\/a> stated, machine learning (a subset of AI) shows promising results in cardiac imaging by improving decision-making based on identified data patterns. Deep learning, inspired by the human brain's processing capability, takes this a step further. As these technologies advance, providing transparency into what tools are being used to review and evaluate cardiac imaging becomes imperative for both physicians and patients.<\/p> \u00a0<\/p> For widespread use, AI tools must be readily accessible and offer physicians the control to decide where and how to apply the resultant information.<\/p> \u00a0<\/p> The future of AI in cardiac imaging, like many new technologies, is teeming with innovative tools. The challenge for the medical imaging industry is to ensure these tools enhance practitioners' workflow rather than impede it, while also facilitating broad availability to physicians in institutions of various sizes, private practices, and imaging centers.<\/p> \u00a0<\/p> The onus may fall on the existing medical imaging industry players to resolve these challenges. They could draw inspiration from other industries that have seamlessly integrated emerging technologies into everyday life, such as Amazon. Starting as a platform for books, Amazon has evolved into a streamlined, user-friendly platform where customers can compare options, enhancing their trust and reliance on the technology. Consequently, a plausible direction for AI in cardiac imaging could be the creation of a platform that simplifies access to AI tools as much as placing a one-click Amazon order or purchasing through Apple Pay. The potential impact of such an innovation on facilities of all sizes and patient care is significant.<\/p> \u00a0<\/p> \u00a0<\/p> Laurie Smith is a principal and COO at Core Sound Imaging, Inc.\u2014makers of the Studycast System, a comprehensive imaging workflow platform. The Studycast System is a platform for medical imaging workflow that has been disrupting and streamlining medical image storage and reporting for 15 years. Studycast is connecting physicians to their images and interpretation tools from any Internet-connected device.<\/em><\/p> \u00a0<\/p> [1]<\/a> Artificial Intelligence Will Transform Cardiac Imaging\u2014Opportunities and Challenges<\/p> Steffen E. Petersen<\/a>,,* Musa Abdulkareem<\/a>, and Tim Leiner<\/a><\/p>The Promise of Clarity: Ultra-High Resolution Imaging<\/span><\/h3>\n
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Imaging at Your Fingertips: The Rise of Portable Point-of-Care Solutions<\/span><\/h3>\n
The Foundation for Intelligent Insights: Cardiac Imaging Excellence and AI<\/span><\/h3>\n
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Superior Cardiac Care and the Role of Core Sound Imaging<\/span><\/h3>\n
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