Real-time AI that prevents intradialytic hypotension, enabling safer and more personalised dialysis care.
Real-time AI that prevents intradialytic hypotension, enabling safer and more personalised dialysis care.
Real-time AI that prevents intradialytic hypotension, enabling safer and more personalised dialysis care.
About Us
Partnership with
DIALIQ™
Predictive Dialysis Care
DIALIQ™
Dialysis Care
Continuously monitors live patient data, predicts intradialytic hypotension before onset, and dynamically adjusts ultrafiltration
Continuously monitors live patient data, predicts intradialytic hypotension before onset, and dynamically adjusts ultrafiltration
Clinical Development & Deployment Pipeline
Clinical Development Pipeline
We transform raw dialysis data (BP, HR, UFR) into actionable intelligence ensuring safer fluid removal and better patient outcomes.
We transform raw dialysis data (BP, HR, UFR) into actionable intelligence ensuring safer fluid removal and better patient outcomes.
Step 1
Clinical Data Collection
Collecting multi-parameter dialysis data (blood pressure, heart rate, UFR, and machine settings) in a secure, compliant manner to build a robust foundation for predictive modeling.
Gathering Data..
Blood Pressure
Filtration Rate
Patient Weight
Dialyser Type
Blood Flow
Gathering Data..
Blood Pressure
Filtration Rate
Patient Weight
Dialyser Type
Blood Flow
Step 2
AI Development
Our LSTM-based algorithm is trained on real-world retrospective clinical datasets. The model predicts intradialytic hypotension minutes before onset, enabling proactive intervention.
- class UFRPredictor(nn.Module):def __init__(self, input_size=11):super().__init__()self.lstm = nn.LSTM(input_size, hiddenbatch_first=True)self.fc = nn.Linear(hidden_size, 1)def forward(self, x):out, _ = self.lstm(x)return torch.sigmoid(self.fc(out[:, -1, :]
- class UFRPredictor(nn.Module):def __init__(self, input_size=11):super().__init__()self.lstm = nn.LSTM(input_size, hiddenbatch_first=True)self.fc = nn.Linear(hidden_size, 1)def forward(self, x):out, _ = self.lstm(x)return torch.sigmoid(self.fc(out[:, -1, :]
- class UFRPredictor(nn.Module):def __init__(self, input_size=11):super().__init__()self.lstm = nn.LSTM(input_size, hiddenbatch_first=True)self.fc = nn.Linear(hidden_size, 1)def forward(self, x):out, _ = self.lstm(x)return torch.sigmoid(self.fc(out[:, -1, :]
- class UFRPredictor(nn.Module):def __init__(self, input_size=11):super().__init__()self.lstm = nn.LSTM(input_size, hiddenbatch_first=True)self.fc = nn.Linear(hidden_size, 1)def forward(self, x):out, _ = self.lstm(x)return torch.sigmoid(self.fc(out[:, -1, :]
Step 3
Seamless System Integration
A fully functional GUI is ready for real-time monitoring and UFR adjustment simulation. API integration is in progress, enabling easy deployment into dialysis workflows and EHR systems.
DIALIQ™ API
Hospital EMR
DIALIQ™ API
Hospital EMR
Step 4
Real-World Testing & Iteration
We are preparing a prospective pilot study with nephrologists to measure patient outcomes. The model will be continuously refined using clinician feedback and outcome data — ensuring safety, accuracy, and regulatory compliance
Pilot Study
Prospective validation with real patient sessions.
Clinician Feedback
Refinement driven by nephrologist and dialysis nurse
Regulatory Ready
Designed to support SaMD approval
Pilot Study
Prospective validation with real patient sessions.
Clinician Feedback
Refinement driven by nephrologist and dialysis nurse
Regulatory Ready
Designed to support SaMD approval
Step 1
Clinical Data Collection
Collecting multi-parameter dialysis data (blood pressure, heart rate, UFR, and machine settings) in a compliant manner to build a robust foundation for predictive modeling.
Gathering Data..
Blood Pressure
Filtration Rate
Patient Weight
Dialyser Type
Blood Flow
Gathering Data..
Blood Pressure
Filtration Rate
Patient Weight
Dialyser Type
Blood Flow
Step 2
AI Development
Our Deep learning based algorithm is trained on real-world retrospective clinical datasets.
- class UFRPredictor(nn.Module):def __init__(self, input_size=11):super().__init__()self.lstm = nn.LSTM(input_size, hiddenbatch_first=True)self.fc = nn.Linear(hidden_size, 1)def forward(self, x):out, _ = self.lstm(x)return torch.sigmoid(self.fc(out[:, -1, :]
- class UFRPredictor(nn.Module):def __init__(self, input_size=11):super().__init__()self.lstm = nn.LSTM(input_size, hiddenbatch_first=True)self.fc = nn.Linear(hidden_size, 1)def forward(self, x):out, _ = self.lstm(x)return torch.sigmoid(self.fc(out[:, -1, :]
- class UFRPredictor(nn.Module):def __init__(self, input_size=11):super().__init__()self.lstm = nn.LSTM(input_size, hiddenbatch_first=True)self.fc = nn.Linear(hidden_size, 1)def forward(self, x):out, _ = self.lstm(x)return torch.sigmoid(self.fc(out[:, -1, :]
- class UFRPredictor(nn.Module):def __init__(self, input_size=11):super().__init__()self.lstm = nn.LSTM(input_size, hiddenbatch_first=True)self.fc = nn.Linear(hidden_size, 1)def forward(self, x):out, _ = self.lstm(x)return torch.sigmoid(self.fc(out[:, -1, :]
Step 3
EMR Integration
DIALIQ™ connects to hospital EMR and dialysis machine data through a secure API, enabling real-time risk scoring and actionable decision support within existing clinical workflows.
DIALIQ™ API
Hospital EMR
DIALIQ™ API
Hospital EMR
Step 4
Real-World Testing & Iteration
We are preparing a prospective pilot study with nephrologists to measure patient outcomes. The model will be continuously refined using clinician feedback and outcome data
Pilot Study
Prospective validation with real patient sessions.
Clinician Feedback
Refinement driven by nephrologist and dialysis nurse
Regulatory Ready
Designed to support SaMD approval
Pilot Study
Prospective validation with real patient sessions.
Clinician Feedback
Refinement driven by nephrologist and dialysis nurse
Regulatory Ready
Designed to support SaMD approval
Impact of DIALIQ™ on Patient Outcomes
Improve Haemodynamic Stability
DIALIQ analyses live dialysis signals to detect early signs of IDH. This early insight supports steadier blood pressure control, reducing symptomatic drops and improving treatment tolerance.
Reduce Sudden Cardiovascular Risk
By minimising rapid blood pressure declines, DIALIQ reduces cardiac stress and supports safer long-term cardiovascular outcomes.
Request Early Access to Nexev
Be among the first dialysis centers to test AI-guided, real-time UFR optimisation. Let's make dialysis safer — together
Request Early Access to Nexev
Be among the first dialysis centers to test AI-guided, real-time UFR optimisation. Let's make dialysis safer — together
founder@nexev.co
