Extended intelligence for cardiac implantable electronic device (CIED) placement procedures
Novel tools and techniques are provided for implementing intelligent assistance (“IA”) or extended intelligence (“EI”) ecosystem to placement procedures for cardiac implantable electronic device (“CIED”). In various embodiments, a computing system might analyze received one or more first layer input data (i.e., room content-based data) and received one or more second layer input data (i.e., patient and/or tool-based data), and might generate one or more recommendations for guiding a medical professional in performing a CIED placement procedure in a heart of the patient, based at least in part on the analysis, the generated one or more recommendations comprising 3D or 4D mapped guides toward, in, and around the heart of the patient. The computing system might then generate one or more XR images, based at least in part on the generated one or more recommendations, and might present the generated one or more XR images using a UX device.
1 . A method, comprising:
receiving, using a computing system and during a cardiac implantable electronic device (“CIED”) placement procedure for a patient, one or more first layer input data from one or more first devices, the one or more first layer input data comprising at least one of movement data, position data, relative distance data, or externally observable data, wherein the at least one of movement data, position data, relative distance data, or externally observable data pertains to each of one or more persons and one or more objects within a real-world clinical environment for performing the CIED placement procedure, wherein the one or more persons include medical staff other than a medical professional performing the CIED placement procedure;
receiving, using the computing system, one or more second layer input data from one or more second devices, the one or more second layer input data comprising at least one of one or more patient sensor data for monitoring procedure-relevant aspects of the patient, one or more patient imaging data for monitoring images of one or more portions of a body of the patient, or one or more navigation and mapping data for monitoring one or more surgical devices relative to the one or more portions of the body of the patient;
analyzing, using the computing system, the received one or more first layer input data and the received one or more second layer input data;
generating, using the computing system, one or more recommendations for guiding the medical professional in performing the CIED placement procedure in a heart of the patient, based at least in part on the analysis, the generated one or more recommendations comprising three-dimensional (“3D”) or four-dimensional (“4D”) mapped guides toward, in, or around the heart of the patient, the 3D or 4D mapped guides comprising one or more graphical bounds for the CIED or a trajectory of the CIED to guide the medical professional in performing the CIED placement procedure;
generating, using the computing system, one or more extended reality (“XR”) images, based at least in part on the generated one or more recommendations;
presenting, using the computing system and using a user experience (“UX”) device, the generated one or more XR images; and
generating, using the computing system, one or more control instructions to at least one robotic system based at least in part on the analysis of the received one or more first layer input data and the received one or more second layer input data.
2 . The method of claim 1 , wherein the computing system comprises at least one of an XR computing system, a medical procedure computing system, a hub computing system, a 3D graphical processing unit, a cluster computing system, a 4D graphics computing system, a server computer, a cloud computing system, or a distributed computing system.
3 . The method of claim 1 , wherein the one or more surgical devices comprise at least one of one or more catheters, one or more catheter interconnect cables, one or more leads, one or more pacemakers, one or more defibrillators, one or more rigid robotic devices, one or more soft robotic devices, one or more robotic systems, one or more robotic arms, one or more needles, one or more therapeutic delivery devices, one or more implant delivery devices, one or more diagnostic devices, one or more diagnostic catheters, one or more implant devices, one or more surgical tools, one or more monitoring devices, one or more cameras, one or more imaging tools, one or more fiducials, one or more staples, one or more anchors, one or more embolic protection devices, one or more cardiomyoplasty tools, one or more vascular closure tools, one or more septal closure tools, one or more guide wires, one or more introducers, one or more sheaths, an implantable cardioverter defibrillator (“ICD”) device, an extravascular ICD (“EV-ICD”), a miniature leadless implant, a conduction system pacing (“CSP”) device, an implantable bradycardia pacemaker with leads, a leadless bradycardia pacemaker, a low power implantable cardiac resynchronization therapy (“CRT-P”) device, a high power implantable cardiac resynchronization therapy (“CRT-D”) device, or one or more capital equipment.
4 . The method of claim 1 , wherein the one or more patient sensor data are obtained using one or more sensors comprising at least one of one or more chronically implanted sensors, one or more diagnostic sensors, one or more surgical sensors, one or more wearable sensors, one or more gas sensors, one or more optical sensors, one or more impedance sensors, one or more ultrasound sensors, one or more flow sensors, one or more blood velocity sensors, one or more blood volume sensors, one or more electrical sensors, one or more voltage sensors, one or more amperage sensors, one or more wattage sensors, one or more impedance sensors, one or more motion sensors, one or more sound sensors, one or more blood pressure sensors, one or more heart rate sensors, one or more pulse sensors, one or more oxygen sensors, one or more carbon dioxide (“CO 2 ”) sensors, one or more fluid levels, one or more doppler sensors, one or more biomarker sensors, one or more perfusion sensors, one or more electromyography (“EMG”) sensors, one or more sleep sensors, one or more cardiac hemodynamics sensors, one or more ischemia sensors, one or more hematocrit (“HCT”) level sensors, one or more biometric sensors, one or more electroencephalographic (“EEG”) sensors, or one or more pain sensors.
5 . The method of claim 1 , wherein the one or more patient imaging data are obtained using one or more imaging devices comprising at least one of a magnetic resonance imaging (“MRI”) system, a computed tomography (“CT”) system, an ultrasound (“US”) system, an electromechanical wave imaging (“EWI”) system, a magnetic resonance angiography (“MRA”) system, a computed tomography angiography (“CTA”) system, a blood oxygen-level dependent signal (“BOLD”) system, an electroencephalography (“EEG”) system, an optical coherence tomography (“OCT”) system, a dynamic susceptibility contrast (“DSC”) MRI system, a fluoroscopy system, an X-ray system, or an endoscopy system.
6 . The method of claim 1 , wherein the CIED placement procedure comprises at least one of a cardiac mapping procedure, a cardiac resynchronization therapy (“CRT”) device installation procedure, a right ventricular pacing (“RVP”) procedure, a His bundle pacing (“HBP”) procedure, a left bundle branch pacing (“LBBP”) lead placement procedure, a right bundle branch pacing (“RBBP”) lead placement procedure, a bilateral bundle branch area pacing (“BBBP”) lead placement procedure, an implant procedure, an implantable cardioverter defibrillator (“ICD”) device installation procedure, an extravascular ICD (“EV-ICD”) device installation procedure, a pacemaker installation procedure, a miniature leadless implant installation procedure, or a remote monitoring device installation procedure.
7 . The method of claim 1 , wherein the one or more XR images comprise at least one of one or more augmented reality (“AR”) images, one or more AR videos, one or more virtual reality (“VR”) images, one or more VR videos, one or more mixed reality (“MR”) images, one or more MR videos, one or more XR images, or one or more XR videos.
8 . The method of claim 1 , wherein the UX device comprises at least one of a headset, UX glasses, a viewing window, a supplement to existing glasses, headphones, UX contact lenses, a heads-up display (“HUD”) device, a 3D spatial sound system, a telemonitoring system, a rigid robotic device control and sensory feedback system, a soft robotic device control and sensory feedback system, an eye control system, a voice control system, a remote control system, a gesture-based control system, a sign language-based control system, a body-part-based control system, a joystick, a mouse, a two-dimensional (“2D”) screen display, a 3D refractive display, a parallel reality system, a projection system, a 3D printed reconstruction system, a customized view generation system, a ghosting and prediction system, a master-slave control system, an annotation system, or a haptic feedback system.
9 . The method of claim 1 , wherein the generated one or more XR images are presented to provide one or more of: a guide for the medical professional, a navigation tool during the CIED placement procedure, a proximity detection tool during the CIED placement procedure, a 3D or 4D visualization view of the at least one or more portions of the patient, a heads-up display of at least one of the one or more first layer input data, a heads-up display of at least one of the one or more patient sensor data, a heads-up display of at least one of the one or more patient imaging data, a heads-up display of physiological data of the patient, or a heads-up display of procedure-related data of the patient.
10 . The method of claim 1 , further comprising:
tracking, using the computing system, the one or more surgical devices, using at least one of an electropotential-based tracking system, an impedance-based tracking system, an electromagnetic-based tracking system, a magnetic anomaly detection-based tracking system, a radio frequency identification (“RFID”)-based tracking system, a Bluetooth-based tracking system, a wireless-based tracking system, an optical-based tracking system, a laser-based tracking system, an ultrasound (“US”) imaging-based tracking system, a computer vision-based tracking system, a fluoroscopy-based tracking system, an MRI-based tracking system, an accelerometer-based tracking system, a global positioning system (“GPS”)-based tracking system, an infrared (“IR”)-based tracking system, an ultrasonic sound-based tracking system, a piezoelectric-based tracking system, a simultaneous localization and mapping (“SLAM”)-based tracking system, an acoustic-based tracking system, a radar-based tracking system, a feature identification-based tracking system, a machine learning-based tracking system, a predictive tracking system, a prescriptive tracking system, or a near-field communications-based tracking system.
11 . The method of claim 1 , wherein generating, using the computing system, one or more control instructions to at least one robotic system based at least in part on the analysis of the received one or more first layer input data and the received one or more second layer input data further comprises:
receiving, using the computing system, one or more control inputs from the medical professional;
analyzing, using the computing system, the received one or more control inputs in conjunction with the analysis of the received one or more first layer input data and the received one or more second layer input data;
generating, using the computing system, the one or more control instructions based at least in part on the analysis of the received one or more control inputs in conjunction with the analysis of the received one or more first layer input data and the received one or more second layer input data, the generated one or more control instructions taking into account movement including at least one of movement of the heart and surrounding tissue due to continual beating of the heart and due to one or more of table movement, fluid loss, respiration of the patient, or movement or shifting of at least one portion of the body of the patient; and
sending, using the computing system, the generated one or more control instructions to the at least one robotic system to cause the at least one robotic system to implement CIED placement within the heart of the patient as part of the CIED placement procedure.
12 . The method of claim 11 , wherein at least the processes of receiving the one or more first layer input data, the one or more second layer input data, analyzing the received one or more first layer input data and the received one or more second layer input, generating the one or more recommendations, generating the one or more XR images, presenting the generated one or more XR images, receiving the one or more control inputs, analyzing the received one or more control inputs, generating the one or more control instructions, and sending the generated one or more control instructions occur in a manner that is at least one of continual, dynamic, feedback-looped, updated, in real-time, or in near-real-time during the course of the CIED placement procedure.
13 . The method of claim 11 , wherein the received one or more control inputs comprise hand-movement-based control inputs resulting from movement of one or more hands of the medical professional, wherein analyzing the received one or more control inputs comprises determining whether the hand-movement-based control inputs comprise inputs indicative of excessive movement of at least one hand of the one or more hands of the medical professional, and wherein generating the one or more control instructions comprises, based on a determination that the hand-movement-based control inputs comprise inputs indicative of excessive movement of at least one hand of the medical professional, generating, using the computing system, one or more compensated control instructions that include control instructions that are based on hand-movement-based control inputs while dampening one or more particular control inputs that are based on excessive movement of the at least one hand of the medical professional.
14 . The method of claim 1 , wherein the method is performed without use of fluoroscopy.
15 . The method of claim 1 , wherein the generated one or more recommendations comprise 3D or 4D mapped guides toward, in, and around the heart of the patient.
16 . An apparatus, comprising:
at least one processor; and
a non-transitory computer readable medium communicatively coupled to the at least one processor, the non-transitory computer readable medium having stored thereon computer software comprising a set of instructions that, when executed by the at least one processor, causes the apparatus to:
receive, during a cardiac implantable electronic device (“CIED”) placement procedure for a patient, one or more first layer input data from one or more first devices, the one or more first layer input data comprising at least one of movement data, position data, relative distance data, or externally observable data, wherein the at least one of movement data, position data, relative distance data, or externally observable data pertains to each of one or more persons and one or more objects within a real-world clinical environment for performing the CIED placement procedure, wherein the one or more persons include medical staff other than a medical professional performing the CIED placement procedure;
receive one or more second layer input data from one or more second devices, the one or more second layer input data comprising at least one of one or more patient sensor data for monitoring procedure-relevant aspects of the patient, one or more patient imaging data for monitoring images of one or more portions of a body of the patient, or one or more navigation and mapping data for monitoring one or more surgical devices relative to the one or more portions of the body of the patient;
analyze the received one or more first layer input data and the received one or more second layer input data;
generate one or more recommendations for guiding the medical professional in performing the CIED placement procedure in a heart of the patient, based at least in part on the analysis, the generated one or more recommendations comprising three-dimensional (“3D”) or four-dimensional (“4D”) mapped guides toward, in, or around the heart of the patient, the 3D or 4D mapped guides comprising one or more graphical bounds for the CIED or a trajectory of the CIED to guide the medical professional in performing the CIED placement procedure;
generate one or more extended reality (“XR”) images, based at least in part on the generated one or more recommendations;
present, using a user experience (“UX”) device, the generated one or more XR images; and
generate one or more control instructions to at least one robotic system based at least in part on the analysis of the received one or more first layer input data and the received one or more second layer input data.
17 . The apparatus of claim 16 , wherein the generated one or more recommendations comprise 3D or 4D mapped guides toward, in, and around the heart of the patient.
18 . A system, comprising:
a computing system, comprising:
at least one first processor; and
a first non-transitory computer readable medium communicatively coupled to the at least one first processor, the first non-transitory computer readable medium having stored thereon computer software comprising a first set of instructions that, when executed by the at least one first processor, causes the computing system to:
receive, during a cardiac implantable electronic device (“CIED”) placement procedure for a patient, one or more first layer input data from one or more first devices, the one or more first layer input data comprising at least one of movement data, position data, relative distance data, or externally observable data, wherein the at least one of movement data, position data, relative distance data, or externally observable data pertains to each of one or more persons and one or more objects within a real-world clinical environment for performing the CIED placement procedure, wherein the one or more persons include medical staff other than a medical professional performing the CIED placement procedure;
receive one or more second layer input data from one or more second devices, the one or more second layer input data comprising at least one of one or more patient sensor data for monitoring procedure-relevant aspects of the patient, one or more patient imaging data for monitoring images of one or more portions of a body of the patient, or one or more navigation and mapping data for monitoring one or more surgical devices relative to the one or more portions of the body of the patient;
analyze the received one or more first layer input data and the received one or more second layer input data;
generate one or more recommendations for guiding the medical professional in performing the CIED placement procedure in a heart of the patient, based at least in part on the analysis, the generated one or more recommendations comprising three-dimensional (“3D”) or four-dimensional (“4D”) mapped guides toward, in, or around the heart of the patient, the 3D or 4D mapped guides comprising one or more graphical bounds for the CIED or a trajectory of the CIED to guide the medical professional in performing the CIED placement procedure;
generate one or more extended reality (“XR”) images, based at least in part on the generated one or more recommendations;
present, using a user experience (“UX”) device, the generated one or more XR images; and
generate one or more control instructions to at least one robotic system based at least in part on the analysis of the received one or more first layer input data and the received one or more second layer input data.
19 . The system of claim 18 , wherein the generated one or more recommendations comprise 3D or 4D mapped guides toward, in, and around the heart of the patient.