Cardiac imaging enables clinicians to visualize intricate anatomical structures and assess dynamic functional parameters. The wealth of information captured through cardiac imaging forms a critical input for artificial intelligence (AI).<\/span><\/p>\n AI will enhance imaging capabilities so clinicians have advanced tools and insights to analyze cardiac data and improve their diagnostic workflows. However, AI\u2019s true potential in cardiology hinges on a key prerequisite: imaging excellence. The more precise and reliable the images are, the more valuable and dependable AI analysis and insights will be.<\/span><\/p>\n While cardiac imaging provides clinicians with the necessary information to diagnose and create treatment plans, its limitations could hinder AI-driven diagnostic accuracy and effectiveness. Traditional methods rely heavily on the person conducting the test, and different experts may interpret the same results differently. Manually segmenting cardiac structures to quantify diagnoses is labor-intensive and prone to inconsistencies. Depending on the modality and technical parameters, image quality can be inconsistent and impacted by noise and poor resolution.<\/span><\/p>\n The lack of data formats and standardized protocols across institutions and imaging modalities is also a significant hurdle to training robust, universal AI algorithms. Due to the volume of data generated during routine echocardiography, diagnostic information may be inadvertently underutilized. It often exceeds the capacity of human experts to interpret comprehensively within a limited timeframe.<\/span><\/p>\n This is where the concept of imaging excellence, exemplified by solutions such as Core Sound Imaging’s Studycast, becomes essential. Clinicians need a system that doesn\u2019t compromise on image quality or workflow efficiency, and the system must understand their clinical cardiology needs. The system must prioritize speed and performance because fast access to high-quality images is crucial when providing care for cardiac patients. Clinicians require an intuitive platform to streamline their imaging workflows so it is easy to view images quickly and efficiently, document findings, and generate reports. Importantly, they need a system that supports accessibility, enabling secure access to images and cine loops from any device at any time to facilitate remote consultations and efficient reviews.<\/span><\/p>\n However, their true commitment to imaging excellence lies in how that commitment directly fuels the evolution of cardiological AI toward a singular, integrated workflow.<\/span><\/p>\n While AI holds immense transformative potential in cardiac imaging, its effectiveness is inextricably linked to the quality of the input imagery.\u00a0<\/span><\/p>\n Platforms committed to excellence in imagery, speed, usability, and seamless integration, such as Core Sound Imaging\u2019s Studycast, enhance current diagnostic workflows and actively fuel the evolution of cardiological AI. By providing standardized, high-quality data in one environment, Studycast helps AI move beyond theoretical possibilities for clinicians and become a tangible, indispensable tool. This tool ultimately will help clinicians offer more personalized treatments, more accurate diagnoses, and improved outcomes for their patients with cardiovascular disease. The more precise the picture that excellent cardiac imaging provides, the more innovative and impactful AI-driven diagnosis will be to usher in a new era of precision cardiology.<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” global_colors_info=”{}”]<\/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 need exceptional cardiac imaging quality to experience the full benefits of AI in the imaging process Cardiac imaging enables clinicians to visualize intricate anatomical structures and assess dynamic functional parameters. The wealth of information captured through cardiac imaging forms a critical input for artificial intelligence (AI). AI will enhance imaging capabilities so clinicians have […]<\/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>\u00a0<\/h3>\n
How Integrated Imaging Solutions Unlock More Efficient Care<\/span><\/h3>\n
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Imaging Excellence Will Drive the Future of Cardiology<\/span><\/h3>\n
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