Bringing CT scans and associated clinical information together for machine learning and precise, reliable diagnosis.

Following its success in helping back pain sufferers, MediChain, the medical blockchain big data platform, will next be applying a similar approach to help aneurysm patients.

These are just the first examples of the progress MediChain intends to facilitate worldwide for all significant health problems, using the enormous mass of existing and growing medical data to allow doctors and scientists to make the next set of breakthroughs in relieving what it describes as unnecessary suffering and avoidable premature death.

By bringing dispersed medical records together, including scans, test results, notes of symptoms, treatments given and patient outcomes, MediChain will facilitate progress and breakthroughs across medical research and treatment development.

Patients will have access to and control over their own records, in order to ensure their own doctors are able to make properly informed decisions about their treatment, and in order to contribute anonymized data for big data to assist in the development of improved treatments.

Bringing patient data together using its MVP has already resulted in the discovery of a low cost intervention for back pain. Now, through its partnership with Geneva University Hospital, the aim is to improve the diagnosis and management of aneurysmal subarachnoid haemorrhage, a type of stroke caused by bleeding on the surface of the brain. This will help classify patients, and determine treatments, in a way that is more sophisticated and reliable than current methods. Currently around half of all cases are fatal, and people who survive can be left with long-term problems, so improved diagnosis and management is much needed.

To make this possible, CT scans from different patients have been brought together, along with clinical data linked to each scan, to form a training dataset for analysis. By analysing the scans and associated data, the machine learning will learn how specific features in the scans link with deaths and key clinical events, in order to diagnose the severity of ruptures it sees in new scans.

Doctors will upload a patient’s CT scan along with patient details, and the scan will go through the machine learning classifiers. This objective, machine-learned classification will be much more helpful than the currently used, subjective scoring, where the doctor looks at the scan and assigns one of four scores.

The current scoring is not only coarse grained, but is subject to significant measurement error and relies on the individual doctor to link what is seen in the scan with how best to treat the patient.

The machine-learned score will be more precise and will eliminate human error both in measurement and in assessment of what is likely to happen next for the patient and what treatment is needed, improving outcomes for patients and their families. In some cases doctors may even be able to treat for expected complications before they occur.

This will also allow for the eventual standardization of clinical practice, allowing best practice to be spread for the benefit of patients elsewhere, in line with MediChain’s wider goals.