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Adelle Diagnostics

Adelle Diagnostics develops AI-driven, blood-based diagnostic tests for neurodegenerative diseases like Normal Pressure Hydrocephalus (NPH). Their platform uses multi-omics data and machine learning to accurately diagnose NPH and predict the success of shunt surgery, improving patient stratification and treatment decisions.

Providence, United States250+ followers
Updated 2 months ago

Funding

Funding not disclosed

Funding rounds are not available yet.

Founders

Product

Problem

Accurate diagnosis of Normal Pressure Hydrocephalus (NPH) is challenging due to symptom overlap with other neurodegenerative conditions, leading to underdiagnosis and a 50% rate of ineffective shunt surgeries. The lack of predictive tests for surgical outcomes places patients at risk and complicates treatment decisions.

Solution

Adelle Diagnostics offers a high-throughput, AI-driven platform for developing minimally invasive blood-based diagnostic tests for neurodegenerative diseases, specifically targeting NPH. The platform integrates multi-omics data, including next-generation RNA sequencing and high-resolution mass spectrometry, to identify and validate biomarkers. Machine learning algorithms analyze these comprehensive molecular signatures to provide accurate NPH diagnosis and predict the efficacy of shunt surgery. This precision medicine approach aims to improve patient outcomes by enabling earlier, more informed clinical decision-making.

Target Audience

The primary customers are neurologists, neurosurgeons, and healthcare systems seeking advanced diagnostic tools for neurodegenerative diseases, particularly NPH, to improve patient stratification and treatment efficacy.

Features

  • AI-powered platform for biomarker identification and validation using multi-omics data.
  • Minimally invasive blood-based diagnostic tests for neurodegenerative diseases.
  • Specific application for NPH diagnosis and prediction of shunt surgery benefit.
  • Integration of next-generation RNA sequencing and high-resolution mass spectrometry for comprehensive molecular profiling.
  • Machine learning algorithms for analysis of complex biological datasets.
  • Precision medicine approach to support clinical decision-making.
This profile is AI-generated and may contain inaccuracies.