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Xen.AI Medical is a suite of  Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning based solutions for the Medical and Healthcare sector.


Hospitals and medical labs generate enormous amounts of data.  The volume of data produced is expected to increase at a CAGR of 36% through 2025.  Automation and machine learning will help reduce turnaround time for analysis of these large datasets.  This is critical in areas where a timely response can save a life as in an ICU.  Automating analysis of historical time series data, predicting diagnosis from radiology imaging and predicting propensity to illness and hospitalization will help physicians make effective decisions.

The cost of healthcare in US outstrips inflation.  The bulk of this burden is pushed to patients.  An aging population adds to healthcare burden.  Optimization of operational costs through better predictive modeling and analytics will become necessary for hospitals to help reduce cost to patients.

Goals of Xen.AI Medical Solution

  1. Disease detection and diagnosis from radiology imaging.  This encompasses a vast array of diseases like screening for cancers, cardiovascular abnormalities, detection of musculoskeletal injuries, neurological and pulmonary problems.
  2. Improve ICU care by providing faster response times for large dataset analysis and anomaly detection.
  3. Identify pre existing conditions from genetic data and update with new research.
  4. Combine machine learning models in Xen.AI Medical with Xen applications to make precision medicine recommendations.

Xen.AI Medical Solution Overview

Medical Imaging

Diagnostic medical imaging or diagnostic radiology consists of the following technologies today:

2D and 3D images from radiology and nuclear imaging aids doctors to diagnose diseases.  It helps to monitor your body’s response to treatment as well as screen for possible illnesses like breast cancer, lung cancer and heart disease.

Xen.AI Medical is a suite of applications where each application serves a specific purpose be it diagnosis, prevention or cure.  In the area of imaging diagnosis, the following applications can be prioritized:

  1. Screening for cancers:  Lung cancer detection from lung scans (MRI, CT and PET).  Lung cancer has a high mortality rate in later stages of cancer.  Five year survival rate for lung cancer based on stages - 35% (Stage 1), 20% (Stage 2), 6% (Stage 3) and 0% (Stage 4).  It makes a big difference to survival the earlier lung cancer is detected.  We will prioritize lung cancer detection from scans and continue with a suite of models for each cancer type.
  2. Cardiovascular abnormalities from heart structure measurements:  Heart structure and function can help in early detection of cardiovascular diseases as well as estimate risk of cardiac arrest.  In addition to imaging, EKG measurements may also be used to aid diagnosis.
  3. Detection of fractures and musculoskeletal injuries:  These injuries can lead to long term chronic pain if not treated properly.  Detection of fractures (hairline), dislocations and soft tissue injuries could aid physicians in treating these injuries.
  4. Neurological diseases:   Degenerative neurological diseases like ALS if flagged early have the potential to help patients with long term care.  Cognitive impairment in Alzheimers when detected early has the potential to be managed better - Alzheimer’s Disease Neuroimaging Initiative is one such project.
  5. Pulmonary diseases: Pneumonia and Pneumothorax are conditions that require quick response.  Detection of these diseases when there are pre-existing conditions like cystic fibrosis become hard.  AI can assist radiologists in effective detection of these diseases.

Intensive Care Unit (ICU) Data Analytics

Critical care decisions sometimes are made with a high degree of uncertainty and physicians have very little time to make a decision.  There is little data in the value of treatments and interventions in ICU.  A few scoring systems (APACHE, MPM, SAPS) have been used to measure ICU performance (predicted vs outcome) and they have only been used 10% to 15% of the time.  The Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) database is a public database with ICU time series data from various measurements.  It is a good starting place for a schema to model ICU dataset.

There are many potential use cases in ICU Data Analytics applications.  We plan to prioritize the following:

  1. Utilize waveforms and time series data generated from monitors (pulse, oxygen, temperature, pressure) to detect anomalies
  2. Predicting shock and sepsis for hypotension patients
  3. Prediction of heart or lung failure from vitals time series data
  4. Prediction of ventilation related problems like lung infection
  5. Predict length of stay and mortality

Genomic applications

This is another vast area of research with potential for many applications and use cases.  A fully sequenced genome for a patient is a prerequisite to use genomic applications developed by  In addition relevant sequence data from tumors (ctDNA) for oncology applications may be necessary.  Our focus will be clinical applications using patient and population genome data.

The following use cases will be prioritized:

  1. Discovery of mutations for monogenic disorders
  2. Predict drug response side effects
  3. Predict response to therapy from biomarkers in tumors
  4. Predict risk for diseases from population data

Xen.AI Medical Solution Architecture

Key Benefits of Xen.AI Medical Solution

Contact Us




Param Namboodiri

501, Gibson Dr, #2624

Roseville, California - 95678, USA

Phone: +1 408 221 6976


Shanawaz Hakeem                                

ES 11, Heavenly Plaza, Kakkanad                        

Kochi – 682021, Kerala, India

Phone: +91 907 488 7447

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