Cardioai

Artificial Intelligence for Automatic Annotation and Interpretation of Electrocardiograms

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Product Descriptions

Ecg diagnosis cardioai® is a feature-rich productivity tool that accelerates the interpretation of electrocardiograms. it is especially valuable in cases of prolonged or constant cardiac monitoring. remote patient monitoring cardioai® enables better health surveillance using current assets in remote, difficult, or dangerous locations. accurate near real-time processing permits unprecedented medical support. digital health cardioai® can be integrated into an ehr system, or be a part of mobile health device. this legally marketed, off-the-shelf, software can be tailored to fit any business requirement. accurate, fast and productive cardioai® provides accurate and detailed annotation of stress, rest, and holter electrocardiograms according to hl7® aecg standard. benefiting from a deep learning approach, the software detects a wide range of cardiac events that are constantly updating. our powerful platform makes the interpretation of electrocardiograms lightning fast, and even quicker to adapt to new opportunities. the software uses current assets such as existing ecg recorders and infrastructure, and can significantly improve their performance.

How It Works

How it works cardioai® makes electrocardiogram interpretation incredibly easy, and lightning fast. even more, this is a single point of operations that seamlessly integrates all current assets, such as electrocardiographs, holters, event monitors from various manufacturers, a hospital ehr system, and the software to help to evaluate ecg findings. cardioai® is designed to support a comprehensive, integrated, approach to service delivery while paying careful attention to the patient’s experience. the technology enables a physician’s supervision of patients at risk wherever they are. cardioai® is flexible, and can adapt to real-world practice of the pathway from an ecg acquisition to a paper report on a physician's desk; from a regular medical check-up to ambulatory cardiac monitoring during rehab. https://cardio.ai/data/assets/howitworks_1920x1080.png acquiring any digital ecg recorder a patient’s electrocardiogram can be obtained with any legally marketed ecg digital recorder. ecg data acquisition, and the subsequent automatic transfer to cardioai®, are already supported by selected devices. in other cases, native ecg data acquisition software that allows exporting to any supported ecg format, such as scp-ecg, should be used to enable further processing. https://cardio.ai/data/assets/recorders_1920x1080.png uploading uploading through api or web-based platform uploading can be performed automatically if cardioai® has been integrated into an ehr system, or a device with ecg data acquisition software. we recommend using our proprietary format (cai) with lossless compression to minimize data traffic. cardioai® api currently supports scp-ecg and cai formats that can be uploaded to cardioai®. we're constantly working to support more ecg file formats. an electrocardiogram uploading option to cardioai® web platform will be available in the short-term. https://cardio.ai/#/api#a_apioverview https://cardio.ai/#/webapp#a_webappoverview processing the power of artificial neural networks interpretation of an electrocardiogram is about pattern recognition. a cascade of deep learning networks can identify very subtle patterns, which doctors themselves may hardly be aware of. for deep learning to work well, it requires big data. therefore, the data from a ten-year population study has been used to make cardioai® real. all records were initially labelled semi-automatically using the patent pending technology, and then verified by qualified and experienced cardiologists. whereupon true labels were used to train the neural networks. now an ecg interpretation is done with only artificial neural networks. to achieve best results, a record should not be modified by any filter. cardioai® has the prepossessing component that utilizes the benefits of neural networks for removing the high-frequency component of the signal, and providing a baseline wander correction. https://cardio.ai/data/assets/power_1920x1080.png evaluating more possibilities than ever before cardioai® provides an api that can be used to send, retrieve, update, and process virtually all the resources stored and manipulated by the system. this interface incorporates several elements of the rest tradition, making access to these actions consistent and intuitive. cardioai® web, a graphical user interface, utilizes the same api to visualize an ecg record, and for the findings to interpreted by a qualified health care professional. cardioai® api offers four response options that enable our customers to literally build any custom business logic to evaluate ecg findings. the ecg findings stats can be stored in an ehr system’s patient history, which makes it of more value than just an ecg plot. automatically inferred severity rating is to help our customers prioritize ecg records for a cardiac monitoring service. a link to a record at cardioai® web can be stored in an ehr system for further review by the attending physician, or instantly shared between medical doctors of different specialties. an ecg findings report can be retrieved from the system at any time when needed. reporting intelligence and reporting tools cardioai® includes the same analysis and reporting tools as the all-in-one solution. an ecg findings report can be designed using a predefined template and then printed as it looks like on a screen to pdf format. the software automatically identifies and marks the points of the wave components. it also calculates the amplitudes and timing intervals, plots different diagrams and charts, creates tables, renders points of interest in the ecg recording to be added to a report, and much more. the decision-making intelligence allows a physician to see a rationale behind the ai’s decision for a cardiac event as easy as clicking on the info button. the software will mark all the required data on the tracing. the software also allows the physician to suggest a correction, e.g. to classify the event as the dominant beat. built-in medical speech recognition capabilities will be available shortly to accelerate reporting. once a healthcare professional has finalized a report it will be available to download in pdf format. a report can be retrieved in two ways: either downloaded from cardioai® web platform, or requested through cardioai® api. then printing.

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XOresearch

OCR (optical character recognition) is a method of transferring text from image form (such as scan, video or photo) into textual form, that can be processed by machines. ASR (Speech Recognition platform) Cardio.AI

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