Heart FailureOMNIPHYSNCT07814118
Multimodal Physiologic State Intelligence Study
An observational study of Heart Failure and Cardiomyopathies, run by Truway Health, Inc. at 1 site in United States.
Trial summary
The OMNIPHYS study will investigate whether multimodal ultrasound and other routinely collected clinical and physiologic data can be combined with artificial intelligence to characterize changes in a person's physiologic state over time. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Heart Failure, Cardiomyopathies, Pulmonary Hypertension
- What is being tested?
- OMNIPHYS Multimodal Ultrasound Imaging System
- Type of treatment
- Medical device
- How many people
- 25,000 participants
- Where
- 1 siteUnited States
- Study dates
- 2 Sep 2026to 15 Sep 2046
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: OMNIPHYS will enroll a diverse adult population including healthy volunteers and participants receiving routine clinical evaluation or care. The study will include individuals across cardiovascular, vascular, pulmonary, renal, and systemic physiologic conditions, with disease-specific and physiologic subcohorts analyzed within a common longitudinal framework. Multimodal ultrasound, Doppler, physiologic measurements, clinical data, laboratory data, and longitudinal outcomes will be evaluated to characterize individual physiologic states, trajectories, transitions, treatment responses, and recovery patterns.
Who may be able to join
- Adults aged 18 years or older who are capable of providing informed consent, or who meet applicable legally authorized representative requirements where permitted.
- Individuals undergoing routine clinical evaluation, diagnostic imaging, monitoring, treatment, or longitudinal follow-up, including individuals with relevant cardiovascular, vascular, pulmonary, renal, systemic, or other conditions represented within the OMNIPHYS research framework.
- Healthy volunteers without the conditions under study may participate as reference participants.
- Ability to undergo at least one protocol-relevant physiologic or imaging assessment and/or contribute eligible routinely collected clinical data.
- Availability of sufficient clinical, physiologic, imaging, or longitudinal outcome data for the applicable analysis.
- Willingness to permit collection and longitudinal analysis of protocol-defined research data in accordance with informed consent and applicable privacy and data-governance requirements.
Who cannot join
- Inability or unwillingness to provide informed consent or otherwise meet applicable consent requirements.
- Inability to obtain interpretable protocol-relevant physiologic or imaging data when such data are required for the applicable analysis.
- Conditions or circumstances that, in the judgment of the responsible clinical investigator, make participation inappropriate or prevent meaningful longitudinal follow-up.
- Withdrawal of consent or request for discontinuation of research participation where applicable.
- Any circumstance that would prevent collection, secure handling, or analysis of study data in accordance with the approved protocol and applicable requirements.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: OMNIPHYS: A Prospective Multimodal Longitudinal Study of AI-Enabled Physiologic State Modeling and Early Detection of Clinical Deterioration
The OMNIPHYS study will investigate whether multimodal ultrasound and other routinely collected clinical and physiologic data can be combined with artificial intelligence to characterize changes in a person's physiologic state over time. The study will examine cardiac, vascular, pulmonary, and systemic physiologic measurements and develop longitudinal models that describe baseline physiology, physiologic perturbation, compensation, deterioration, treatment response, and recovery. The study is observational and will not assign experimental treatments. The goal is to determine whether changes in multimodal physiologic patterns can be identified and characterized earlier and more reliably than conventional single-time-point assessment.
Read the detailed description
OMNIPHYS: A Prospective Multimodal Longitudinal Study of AI-Enabled Physiologic State Modeling and Early Detection of Clinical Deterioration
The OMNIPHYS study is a prospective, longitudinal observational investigation designed to develop and evaluate a multimodal framework for characterizing human physiologic state and physiologic trajectory over time. The study is centered on the hypothesis that clinically meaningful deterioration may be preceded by measurable changes across multiple physiologic domains, including cardiac function, vascular flow, pulmonary findings, systemic venous congestion, and other routinely available clinical measurements.
The study will collect and analyze multimodal data obtained during clinically appropriate assessments. Ultrasound-derived data may include two-dimensional and three-dimensional imaging, color Doppler, spectral Doppler, cardiac motion and functional measurements, vascular flow characteristics, venous findings, and pulmonary ultrasound observations. Additional clinical data may include vital signs, electrocardiographic measurements, oxygen saturation, laboratory results, medication and intervention records, diagnoses, encounters, and other relevant longitudinal clinical observations.
The study will develop a Physiologic State Vector (PSV) representing multidimensional observations of an individual at defined points in time. Rather than treating disease status as a binary outcome, OMNIPHYS will investigate physiologic states and transitions between states.
The prespecified conceptual state framework includes:
S0 - Baseline: stable observed physiologic phenotype.
S1 - Physiologic Perturbation: measurable deviation from an individual's established baseline.
S2 - Compensation: persistent physiologic abnormality without defined acute clinical deterioration.
S3 - Pre-Decompensation: a longitudinal trajectory associated with increasing probability of clinically significant deterioration.
S4 - Acute Decompensation: clinically meaningful physiologic or clinical deterioration.
S5 - Intervention Response: measurable physiologic change following clinical intervention.
S6 - Recovery: movement toward the participant's prior or expected physiologic state.
S7 - Persistent Dysfunction: sustained deviation from baseline following an acute event or intervention.
The principal research construct is therefore:
Physiologic State → Longitudinal Trajectory → State Transition → Clinical Outcome
The primary investigational performance measure will be the Physiologic Transition Detection Time (PTDT), defined as the interval between a prospectively defined algorithmic detection of a clinically meaningful physiologic transition and the corresponding predefined clinical reference event. PTDT will be evaluated as a research endpoint and will not independently direct clinical care unless separately authorized under an applicable clinical protocol.
Secondary analyses will evaluate physiologic-state classification, trajectory prediction, treatment-response characterization, recovery prediction, longitudinal model calibration, uncertainty estimation, human-AI concordance, cross-site performance, cross-device robustness, and model stability over time.
The OMNIPHYS framework is intended to support multiple pathology-specific cohorts while maintaining a common physiologic modeling architecture. Potential cohorts may include heart failure, cardiomyopathy, pulmonary hypertension, valvular disease, myocarditis, pericardial disease, pulmonary edema, acute respiratory distress syndrome, pulmonary embolic disease, venous thrombosis, sepsis-associated cardiac dysfunction, systemic venous congestion, acute kidney injury, and other clinically appropriate conditions.
A designated research component will investigate federated learning, in which appropriately governed model-development processes may be evaluated across participating institutions without requiring centralized transfer of patient-level datasets. The study will assess whether multimodal physiologic models maintain performance across institutions, ultrasound platforms, acquisition environments, operators, and heterogeneous clinical populations.
The study may additionally investigate a research construct termed the TRUWAY Physiologic Digital Twin, defined as a computational longitudinal representation of observed physiologic measurements and their temporal relationships. This construct is intended for research into trajectory modeling and prediction and does not represent a clinical diagnosis or autonomous clinical decision-making system.
Data interoperability and research infrastructure may incorporate recognized clinical data representations, including DICOM for medical imaging and FHIR-compatible clinical data structures where available and appropriate. Data provenance, version control, quality assurance, model-version tracking, and longitudinal auditability will be incorporated into the research architecture.
The anticipated study horizon is approximately 20 years, from September 2026 through September 2046, permitting investigation of short-term physiologic transitions as well as long-term disease trajectories, recurrence, recovery, persistent dysfunction, technological evolution, and longitudinal model performance.
OMNIPHYS is designed as an observational research protocol. It does not by itself prescribe medical treatment, alter clinical management, or establish that an investigational algorithm is safe or effective for clinical use. Research findings will be evaluated according to the approved protocol, applicable institutional requirements, human-subject protections, data-governance requirements, and prospective statistical analysis plans.
- Conditions
- Heart Failure, Cardiomyopathies, Pulmonary Hypertension, Myocarditis; Acute or Subacute, Myocarditis Due to Drug, Myocarditis Viral
- Being tested
- OMNIPHYS Multimodal Ultrasound Imaging System
- Keywords
- Multimodal Physiologic State Modeling, Physiologic State Transition, Longitudinal Ultrasound, Artificial Intelligence, Machine Learning, Multimodal Imaging, Point-of-Care Ultrasound, 3D Ultrasound, Color Doppler, Doppler Ultrasound, Physiologic Phenotyping, Physiologic Trajectory, Clinical Deterioration, Early Detection, Digital Biomarkers, Federated Learning, Precision Medicine, Longitudinal Monitoring, Cardiac Ultrasound, Vascular Ultrasound
- Medical subject headings
- Heart Failure, Cardiomyopathies, Hypertension, Pulmonary, Myocarditis, Heart Valve Diseases, Hypertension, Sepsis, Acute Kidney Injury
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
OMNIPHYS Multimodal Ultrasound Imaging SystemDevice
Observational physiologic and imaging data acquisition.
| Study group | Role | What they receive |
|---|---|---|
| OMNIPHYS Master Physiologic Cohort | A prospective longitudinal observational cohort of participants undergoing routine clinical evaluation and/or care. The cohort will be characterized using multimodal physiologic observations, including ultrasound-derived imaging, 2D and 3D imaging, color and spectral Doppler, cardiac and vascular measurements, pulmonary ultrasound findings, vital signs, ECG, oxygen saturation, laboratory data, medications, diagnoses, clinical encounters, and longitudinal outcomes. Participants will not be assigned to an experimental treatment. Disease-specific and physiologic subcohorts may be evaluated within the master cohort using a common longitudinal physiologic-state framework. Receives: Device: OMNIPHYS Multimodal Ultrasound Imaging System |
What is being measured
The primary outcome is the main question the study is designed to answer.
Physiologic Transition Detection Time (PTDT)
Time interval, measured in days, between the first prespecified algorithmic detection of a clinically meaningful physiologic state transition and the corresponding predefined clinical reference event. Transitions may include progression from baseline or compensated physiology to pre-decompensation or acute decompensation. The analysis will evaluate the temporal relationship between multimodal physiologic signals and subsequent clinical events.
Measured: Continuous longitudinal assessment from baseline (Year 0) through Year 50; PTDT will be calculated separately for each predefined clinical reference event occurring during the 50-year follow-up period.
Secondary outcomes (9)
Physiologic State Classification Accuracy
Accuracy of classification of predefined longitudinal physiologic states (S0 Baseline, S1 Physiologic Perturbation, S2 Compensation, S3 Pre-Decompensation, S4 Acute Decompensation, S5 Intervention Response, S6 Recovery, and S7 Persistent Dysfunction) using multimodal clinical, physiologic, and ultrasound-derived data.
Measured: Baseline (Year 0) through Year 50; physiologic state-classification accuracy will be assessed using all evaluable longitudinal observations and predefined clinical reference events collected during the 50-year follow-up period.
Physiologic Trajectory Prediction
Performance of longitudinal models in predicting subsequent physiologic state transitions and clinically meaningful deterioration from prior multimodal physiologic observations.
Measured: Baseline (Year 0) through Year 50 of longitudinal follow-up.
Time to Clinical Deterioration
Time from enrollment or a predefined physiologic assessment to the occurrence of a prespecified clinically meaningful deterioration event, evaluated in relation to longitudinal multimodal physiologic trajectories.
Measured: Baseline (Year 0) through Year 50 of longitudinal follow-up.
Treatment Response Characterization
Association between observed clinical interventions and subsequent changes in multimodal physiologic state, including quantitative characterization of physiologic response, non-response, delayed response, and recurrence.
Measured: Baseline (Year 0) through Year 50 of longitudinal follow-up.
Physiologic Recovery Trajectory
Magnitude and rate of movement from an abnormal or decompensated physiologic state toward a predefined recovery state following a clinical event or intervention.
Measured: Baseline (Year 0) through Year 50 of longitudinal follow-up.
Multimodal AI Discrimination Performance
Discrimination, calibration, sensitivity, specificity, and predictive performance of multimodal artificial-intelligence models for identifying predefined physiologic states and clinically meaningful state transitions.
Measured: Baseline (Year 0) through Year 50 of longitudinal follow-up.
Cross-Site and Cross-Device Generalizability
Performance consistency of physiologic-state models across participating clinical sites, ultrasound systems, transducer configurations, acquisition protocols, patient populations, and other relevant data domains.
Measured: Baseline (Year 0) through Year 50 of longitudinal follow-up.
Model Drift and Temporal Stability
Longitudinal assessment of changes in model performance, calibration, data distributions, and physiologic feature distributions over time to characterize temporal robustness and potential model drift.
Measured: Baseline (Year 0) through Year 50 of longitudinal follow-up.
Human-AI Concordance
Agreement between AI-derived physiologic-state classifications and qualified human clinical or imaging assessments using prespecified agreement and concordance measures.
Measured: Baseline (Year 0) through Year 50 of longitudinal follow-up.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Sep 2026
- Study start2 Sep 2026
- Primary completion2 Sep 2046 (estimated)
- Study completion15 Sep 2046 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (10)Papers the sponsor has linked to this study.
- Other trials for Heart FailureStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 25,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Truway Health, Inc.
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
- Investigators
- Gavin C Solomon, M.D., Principal investigator, Truway Health, Inc.
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07814118
- Sponsor's protocol ID
- TH-OMNIPHYS-2026-001
- Acronym
- OMNIPHYS
- Status last verified
- Sep 2026
- First submitted
- 2 Sep 2026
- Last updated
- 10 Sep 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- Yes: Individual participant data (IPD) will be made available to qualified researchers for scientifically valid, methodologically sound secondary research following completion of the primary study analyses and publication of the principal results, subject to applicable ethical, regulatory, privacy, informed-consent, and data-use requirements. De-identified participant-level datasets and accompanying data dictionaries may be shared through a controlled-access process. Requests will be evaluated by the study's designated data-access/governance body based on scientific merit, methodological rigor, feasibility, participant privacy protections, and consistency with the informed consent and applicable institutional requirements. Direct identifiers will not be shared. Data will undergo appropriate de-identification and disclosure-risk assessment before release. Access may require a data-use agreement specifying permitted purposes, security requirements, prohibition of re-identification, restrict
Publications
Kayarian F, Patel D, O'Brien JR, Schraft EK, Gottlieb M. Artificial intelligence and point-of-care ultrasound: Benefits, limitations, and implications for the future. Am J Emerg Med. 2024 Jun;80:119-122. doi: 10.1016/j.ajem.2024.03.023. Epub 2024 Mar 25.
PubMed 38555712Bennett S, Johnson CL, Fisher G, Erskine F, Krasner S, Fletcher AJ, Leeson P. Development and Validation of Echocardiography Artificial Intelligence Models: A Narrative Review. J Clin Med. 2025 Oct 7;14(19):7066. doi: 10.3390/jcm14197066.
PubMed 41096146Koutsoubis N, Waqas A, Yilmaz Y, Ramachandran RP, Schabath MB, Rasool G. Privacy-preserving Federated Learning and Uncertainty Quantification in Medical Imaging. Radiol Artif Intell. 2025 Jul;7(4):e240637. doi: 10.1148/ryai.240637.
PubMed 40366260Rehman MHU, Hugo Lopez Pinaya W, Nachev P, Teo JT, Ourselin S, Cardoso MJ. Federated learning for medical imaging radiology. Br J Radiol. 2023 Oct;96(1150):20220890. doi: 10.1259/bjr.20220890.
PubMed 38011227Guan H, Yap PT, Bozoki A, Liu M. Federated learning for medical image analysis: A survey. Pattern Recognit. 2024 Jul;151:110424. doi: 10.1016/j.patcog.2024.110424. Epub 2024 Mar 12.
PubMed 38559674G S, Gopalakrishnan U, Parthinarupothi RK, Madathil T. Deep learning supported echocardiogram analysis: A comprehensive review. Artif Intell Med. 2024 May;151:102866. doi: 10.1016/j.artmed.2024.102866. Epub 2024 Apr 4.
PubMed 38593684Kim J, Maranna S, Watson C, Parange N. A scoping review on the integration of artificial intelligence in point-of-care ultrasound: Current clinical applications. Am J Emerg Med. 2025 Jun;92:172-181. doi: 10.1016/j.ajem.2025.03.029. Epub 2025 Mar 17.
PubMed 40117961Myhre PL, Grenne B, Asch FM, Delgado V, Khera R, Lafitte S, Lang RM, Pellikka PA, Sengupta PP, Vemulapalli S, Lam CSP. Artificial intelligence-enhanced echocardiography in cardiovascular disease management. Nat Rev Cardiol. 2026 Mar;23(3):164-182. doi: 10.1038/s41569-025-01197-0. Epub 2025 Aug 5.
PubMed 40764834Sahashi Y, Ouyang D, Okura H, Kagiyama N. AI-echocardiography: Current status and future direction. J Cardiol. 2025 Jun;85(6):458-464. doi: 10.1016/j.jjcc.2025.02.005. Epub 2025 Mar 1.
PubMed 40023671Wu D, Arntfield R, Millington SJ. Artificial Intelligence in Point-of-Care Ultrasound. J Intensive Care Med. 2026 Mar 2:8850666261427329. doi: 10.1177/08850666261427329. Online ahead of print.
PubMed 41771537
Study at a glance
From the registry record, last updated by the sponsor on 10 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- OMNIPHYS: A Prospective Multimodal Longitudinal Study of AI-Enabled Physiologic State Modeling and Early Detection of Clinical Deterioration
- ClinicalTrials.gov ID
- NCT07814118
- Acronym / sponsor ID
- OMNIPHYS / TH-OMNIPHYS-2026-001
- Conditions
- Heart Failure, Cardiomyopathies, Pulmonary Hypertension, Myocarditis; Acute or Subacute, Myocarditis Due to Drug, Myocarditis Viral
- Keywords
- Multimodal Physiologic State Modeling, Physiologic State Transition, Longitudinal Ultrasound, Artificial Intelligence, Machine Learning, Multimodal Imaging, Point-of-Care Ultrasound, 3D Ultrasound
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 25,000 participants (estimated)
- Lead sponsor
- Truway Health, Inc.Industry (pharma, biotech, devices)
- Study start
- 2 Sep 2026 (actual)
- Primary completion
- 2 Sep 2046 (estimated)
- Study completion
- 15 Sep 2046 (estimated)
- First posted
- 10 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The OMNIPHYS study will investigate whether multimodal ultrasound and other routinely collected clinical and physiologic data can be combined with artificial intelligence to characterize changes in a person's physiologic state over time. The study will examine cardiac, vascular, pulmonary, and systemic physiologic measurements and develop longitudinal models that describe baseline physiology, physiologic perturbation, compensation, deterioration, treatment response, and recovery. The study is observational and will not assign experimental treatments. The goal is to determine whether changes in multimodal physiologic patterns can be identified and characterized earlier and more reliably than conventional single-time-point assessment.
Detailed description
OMNIPHYS: A Prospective Multimodal Longitudinal Study of AI-Enabled Physiologic State Modeling and Early Detection of Clinical Deterioration
The OMNIPHYS study is a prospective, longitudinal observational investigation designed to develop and evaluate a multimodal framework for characterizing human physiologic state and physiologic trajectory over time. The study is centered on the hypothesis that clinically meaningful deterioration may be preceded by measurable changes across multiple physiologic domains, including cardiac function, vascular flow, pulmonary findings, systemic venous congestion, and other routinely available clinical measurements.
The study will collect and analyze multimodal data obtained during clinically appropriate assessments. Ultrasound-derived data may include two-dimensional and three-dimensional imaging, color Doppler, spectral Doppler, cardiac motion and functional measurements, vascular flow characteristics, venous findings, and pulmonary ultrasound observations. Additional clinical data may include vital signs, electrocardiographic measurements, oxygen saturation, laboratory results, medication and intervention records, diagnoses, encounters, and other relevant longitudinal clinical observations.
The study will develop a Physiologic State Vector (PSV) representing multidimensional observations of an individual at defined points in time. Rather than treating disease status as a binary outcome, OMNIPHYS will investigate physiologic states and transitions between states.
The prespecified conceptual state framework includes:
S0 - Baseline: stable observed physiologic phenotype.
S1 - Physiologic Perturbation: measurable deviation from an individual's established baseline.
S2 - Compensation: persistent physiologic abnormality without defined acute clinical deterioration.
S3 - Pre-Decompensation: a longitudinal trajectory associated with increasing probability of clinically significant deterioration.
S4 - Acute Decompensation: clinically meaningful physiologic or clinical deterioration.
S5 - Intervention Response: measurable physiologic change following clinical intervention.
S6 - Recovery: movement toward the participant's prior or expected physiologic state.
S7 - Persistent Dysfunction: sustained deviation from baseline following an acute event or intervention.
The principal research construct is therefore:
Physiologic State → Longitudinal Trajectory → State Transition → Clinical Outcome
The primary investigational performance measure will be the Physiologic Transition Detection Time (PTDT), defined as the interval between a prospectively defined algorithmic detection of a clinically meaningful physiologic transition and the corresponding predefined clinical reference event. PTDT will be evaluated as a research endpoint and will not independently direct clinical care unless separately authorized under an applicable clinical protocol.
Secondary analyses will evaluate physiologic-state classification, trajectory prediction, treatment-response characterization, recovery prediction, longitudinal model calibration, uncertainty estimation, human-AI concordance, cross-site performance, cross-device robustness, and model stability over time.
The OMNIPHYS framework is intended to support multiple pathology-specific cohorts while maintaining a common physiologic modeling architecture. Potential cohorts may include heart failure, cardiomyopathy, pulmonary hypertension, valvular disease, myocarditis, pericardial disease, pulmonary edema, acute respiratory distress syndrome, pulmonary embolic disease, venous thrombosis, sepsis-associated cardiac dysfunction, systemic venous congestion, acute kidney injury, and other clinically appropriate conditions.
A designated research component will investigate federated learning, in which appropriately governed model-development processes may be evaluated across participating institutions without requiring centralized transfer of patient-level datasets. The study will assess whether multimodal physiologic models maintain performance across institutions, ultrasound platforms, acquisition environments, operators, and heterogeneous clinical populations.
The study may additionally investigate a research construct termed the TRUWAY Physiologic Digital Twin, defined as a computational longitudinal representation of observed physiologic measurements and their temporal relationships. This construct is intended for research into trajectory modeling and prediction and does not represent a clinical diagnosis or autonomous clinical decision-making system.
Data interoperability and research infrastructure may incorporate recognized clinical data representations, including DICOM for medical imaging and FHIR-compatible clinical data structures where available and appropriate. Data provenance, version control, quality assurance, model-version tracking, and longitudinal auditability will be incorporated into the research architecture.
The anticipated study horizon is approximately 20 years, from September 2026 through September 2046, permitting investigation of short-term physiologic transitions as well as long-term disease trajectories, recurrence, recovery, persistent dysfunction, technological evolution, and longitudinal model performance.
OMNIPHYS is designed as an observational research protocol. It does not by itself prescribe medical treatment, alter clinical management, or establish that an investigational algorithm is safe or effective for clinical use. Research findings will be evaluated according to the approved protocol, applicable institutional requirements, human-subject protections, data-governance requirements, and prospective statistical analysis plans.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| OMNIPHYS Master Physiologic Cohort | – | Device: OMNIPHYS Multimodal Ultrasound Imaging SystemA prospective longitudinal observational cohort of participants undergoing routine clinical evaluation and/or care. The cohort will be characterized using multimodal physiologic observations, including ultrasound-derived imaging, 2D and 3D imaging, color and spectral Doppler, cardiac and vascular measurements, pulmonary ultrasound findings, vital signs, ECG, oxygen saturation, laboratory data, medications, diagnoses, clinical encounters, and longitudinal outcomes. Participants will not be assigned to an experimental treatment. Disease-specific and physiologic subcohorts may be evaluated within the master cohort using a common longitudinal physiologic-state framework. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- OMNIPHYS will enroll a diverse adult population including healthy volunteers and participants receiving routine clinical evaluation or care. The study will include individuals across cardiovascular, vascular, pulmonary, renal, and systemic physiologic conditions, with disease-specific and physiologic subcohorts analyzed within a common longitudinal framework. Multimodal ultrasound, Doppler, physiologic measurements, clinical data, laboratory data, and longitudinal outcomes will be evaluated to characterize individual physiologic states, trajectories, transitions, treatment responses, and recovery patterns.
Key inclusion criteria (6 listed)
- Adults aged 18 years or older who are capable of providing informed consent, or who meet applicable legally authorized representative requirements where permitted.
- Individuals undergoing routine clinical evaluation, diagnostic imaging, monitoring, treatment, or longitudinal follow-up, including individuals with relevant cardiovascular, vascular, pulmonary, renal, systemic, or other conditions represented within the OMNIPHYS research framework.
- Healthy volunteers without the conditions under study may participate as reference participants.
- Ability to undergo at least one protocol-relevant physiologic or imaging assessment and/or contribute eligible routinely collected clinical data.
- Availability of sufficient clinical, physiologic, imaging, or longitudinal outcome data for the applicable analysis.
Key exclusion criteria (5 listed)
- Inability or unwillingness to provide informed consent or otherwise meet applicable consent requirements.
- Inability to obtain interpretable protocol-relevant physiologic or imaging data when such data are required for the applicable analysis.
- Conditions or circumstances that, in the judgment of the responsible clinical investigator, make participation inappropriate or prevent meaningful longitudinal follow-up.
- Withdrawal of consent or request for discontinuation of research participation where applicable.
- Any circumstance that would prevent collection, secure handling, or analysis of study data in accordance with the approved protocol and applicable requirements.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| OMNIPHYS Multimodal Ultrasound Imaging SystemObservational physiologic and imaging data acquisition. | Device | OMNIPHYS Master Physiologic Cohort |
Primary outcome
Physiologic Transition Detection Time (PTDT)
Time frame: Continuous longitudinal assessment from baseline (Year 0) through Year 50; PTDT will be calculated separately for each predefined clinical reference event occurring during the 50-year follow-up period.
Time interval, measured in days, between the first prespecified algorithmic detection of a clinically meaningful physiologic state transition and the corresponding predefined clinical reference event. Transitions may include progression from baseline or compensated physiology to pre-decompensation or acute decompensation. The analysis will evaluate the temporal relationship between multimodal physiologic signals and subsequent clinical events.
9 secondary outcomes
Physiologic State Classification Accuracy
Time frame: Baseline (Year 0) through Year 50; physiologic state-classification accuracy will be assessed using all evaluable longitudinal observations and predefined clinical reference events collected during the 50-year follow-up period.
Physiologic Trajectory Prediction
Time frame: Baseline (Year 0) through Year 50 of longitudinal follow-up.
Time to Clinical Deterioration
Time frame: Baseline (Year 0) through Year 50 of longitudinal follow-up.
Treatment Response Characterization
Time frame: Baseline (Year 0) through Year 50 of longitudinal follow-up.
Physiologic Recovery Trajectory
Time frame: Baseline (Year 0) through Year 50 of longitudinal follow-up.
Multimodal AI Discrimination Performance
Time frame: Baseline (Year 0) through Year 50 of longitudinal follow-up.
Cross-Site and Cross-Device Generalizability
Time frame: Baseline (Year 0) through Year 50 of longitudinal follow-up.
Model Drift and Temporal Stability
Time frame: Baseline (Year 0) through Year 50 of longitudinal follow-up.
Human-AI Concordance
Time frame: Baseline (Year 0) through Year 50 of longitudinal follow-up.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Sep 2026.
- Background: PubMed 38555712Kayarian F, Patel D, O'Brien JR, Schraft EK, Gottlieb M. Artificial intelligence and point-of-care ultrasound: Benefits, limitations, and implications for the future. Am J Emerg Med. 2024 Jun;80:119-122. doi: 10.1016/j.ajem.2024.03.023. Epub 2024 Mar 25.
- Background: PubMed 41096146Bennett S, Johnson CL, Fisher G, Erskine F, Krasner S, Fletcher AJ, Leeson P. Development and Validation of Echocardiography Artificial Intelligence Models: A Narrative Review. J Clin Med. 2025 Oct 7;14(19):7066. doi: 10.3390/jcm14197066.
- Background: PubMed 40366260Koutsoubis N, Waqas A, Yilmaz Y, Ramachandran RP, Schabath MB, Rasool G. Privacy-preserving Federated Learning and Uncertainty Quantification in Medical Imaging. Radiol Artif Intell. 2025 Jul;7(4):e240637. doi: 10.1148/ryai.240637.
- Background: PubMed 38011227Rehman MHU, Hugo Lopez Pinaya W, Nachev P, Teo JT, Ourselin S, Cardoso MJ. Federated learning for medical imaging radiology. Br J Radiol. 2023 Oct;96(1150):20220890. doi: 10.1259/bjr.20220890.
- Background: PubMed 38559674Guan H, Yap PT, Bozoki A, Liu M. Federated learning for medical image analysis: A survey. Pattern Recognit. 2024 Jul;151:110424. doi: 10.1016/j.patcog.2024.110424. Epub 2024 Mar 12.
- Background: PubMed 38593684G S, Gopalakrishnan U, Parthinarupothi RK, Madathil T. Deep learning supported echocardiogram analysis: A comprehensive review. Artif Intell Med. 2024 May;151:102866. doi: 10.1016/j.artmed.2024.102866. Epub 2024 Apr 4.
- Background: PubMed 40117961Kim J, Maranna S, Watson C, Parange N. A scoping review on the integration of artificial intelligence in point-of-care ultrasound: Current clinical applications. Am J Emerg Med. 2025 Jun;92:172-181. doi: 10.1016/j.ajem.2025.03.029. Epub 2025 Mar 17.
- Background: PubMed 40764834Myhre PL, Grenne B, Asch FM, Delgado V, Khera R, Lafitte S, Lang RM, Pellikka PA, Sengupta PP, Vemulapalli S, Lam CSP. Artificial intelligence-enhanced echocardiography in cardiovascular disease management. Nat Rev Cardiol. 2026 Mar;23(3):164-182. doi: 10.1038/s41569-025-01197-0. Epub 2025 Aug 5.
- Background: PubMed 40023671Sahashi Y, Ouyang D, Okura H, Kagiyama N. AI-echocardiography: Current status and future direction. J Cardiol. 2025 Jun;85(6):458-464. doi: 10.1016/j.jjcc.2025.02.005. Epub 2025 Mar 1.
- Background: PubMed 41771537Wu D, Arntfield R, Millington SJ. Artificial Intelligence in Point-of-Care Ultrasound. J Intensive Care Med. 2026 Mar 2:8850666261427329. doi: 10.1177/08850666261427329. Online ahead of print.
- PubMed - Artificial Intelligence in Point-of-Care UltrasoundLink provided by the sponsor.
- PubMed - AI-echocardiography: Current status and future directionLink provided by the sponsor.
- PubMed - Artificial intelligence-enhanced echocardiography in cardiovascular disease managementLink provided by the sponsor.
- PubMed - Federated learning for medical image analysis: A surveyLink provided by the sponsor.
- Individual participant dataSharing planned: Yes. Individual participant data (IPD) will be made available to qualified researchers for scientifically valid, methodologically sound secondary research following completion of the primary study analyses and publication of the principal results, subject to applicable ethical, regulatory, privacy, informed-consent, and data-use requirements. De-identified participant-level datasets and accompanying data dictionaries may be shared through a controlled-access process. Requests will be evaluated by the study's designated data-access/governance body based on scientific merit, methodological rigor, feasibility, participant privacy protections, and consistency with the informed consent and applicable institutional requirements. Direct identifiers will not be shared. Data will undergo appropriate de-identification and disclosure-risk assessment before release. Access may require a data-use agreement specifying permitted purposes, security requirements, prohibition of re-identification, restrict
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Gavin C Solomon, M.D. principal investigator
Truway Health, Inc.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Heart Failure and Cardiomyopathies, run by Truway Health, Inc. at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: enrolling by invitation, as of the sponsor's last update on 10 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Physiologic Transition Detection Time (PTDT) (time frame: Continuous longitudinal assessment from baseline (Year 0) through Year 50; PTDT will be calculated separately for each predefined clinical reference event occurring during the 50-year follow-up period.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07814118, which also lists 10 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 Sep 2026.