• Machine Learning
  • Computer aided detection for TB (CAD4TB) installed across Ghana

    A target of sustainable development goals (SDGs) is to end tuberculosis (TB) globally by 2030.  Effective prevention, detection and treatment is necessary to achieve this goal.  Ghana is in the global high burden list for TB, but is tackling this burden using eHealth innovations. 

    In collaboration with Delft Imaging Systems, they have successfully installed 51 X-ray systems in facilities, containers and TB screening mobile clinics across the country.  These mobile X-ray systems are self sustainable, employing solar technology to power them, even in the remotest of locations.  All X-ray systems have been equipped with computer aided detection (CAD4TB) software that makes use of machine learning to detect TB in X-rays.  Additionally, tele-radiology technology is used to interlink all images to a central platform that allows healthcare providers across connected facilities and units to access images.

    The innovation allows healthcare providers to screen up to 200 images per day.  When the images reveal a high CAD4TB score, patients are referred for the standard and more expensive GeneExpert tests.  This makes detecting TB in poorer communities very effective. 

    It is eHealth innovations like this that will strengthen health systems in Ghana and other African countries, while still being conservative of the constrained health budgets in Africa.

  • A smart watch can detect epilepsy

    Epilepsy is a leading serious neurological condition worldwide.  It has particularly significant physical, economic and social consequences.  Recognising the need for an intervention, Empatica Inc. has developed a smart watch to detect seizures in epileptic patients.  They’re calling it Embrace.

    Embrace uses machine learning algorithms to monitor and detect different seizure types, including grand mal or generalised tonic-clonic seizures. Electrodermal Activity (EDA)* sensors in the watch are used to measure multiple indicators of a seizure. 

    It’s also accompanied by an app that will send an alert, via text message, to a healthcare provider or caregiver once a seizure is detected.  Additionally, the app serves as an electronic seizure diary and health record for the user.

    During a clinical study involving 135 epileptic patients, Embrace’s algorithm was shown to detect 100% of the seizures, including the 40% of silent seizures that were unreported in patient clinical diaries.  Following this, the smart watch has received FDA approval as well as approval in Europe as a medical device for epileptic monitoring.

    Embrace’s high sensitivity is revolutionising seizure reporting.  It serves as a much awaited alternative to wearing an EEG, that is automated, and isn’t bulky or cumbersome to wear.

    *signals used to quantify physiological changes in the sympathetic nervous system 

  • Voice recognition reduces Tanzania's patient waiting times

    Patients at the Muhimbili National Hospital in Dar es Salaam no longer have to endure long waiting times for their radiology results.  This is thanks to a new technology installation in the department.  Voice recognition or speech recognition technology is now being used to encode doctors notes on patients so that they can easily be transferred to the radiology department. 

    With this new technology, Tanzanian medical professionals are able to dictate into their computers, in the normal course of speaking and have the speech engine recognise what the clinician wants, and then apply the commands or structured words, respectively, to obtain a radiology report for a patient.  There has been some concern around the effect of speech accents on the technology, but this has posed no problems since implementing it at the hospital.  

    The speech engine is also capable of showing the cardiology report template populated with the name of the patient and other demographic data. By dictating the cardiology report narrative, the computer recognises the narrative context and intent and condenses a complete, correct, and structured document.

    This translates to shorter waiting times for patients, greater operational efficiency within the hospital and reduced workload on medical staff who are required to take notes of patient examinations and consultations.  The technology, which uses natural language processing, is constantly learning speech behaviour through repetitive exposure to terms and complex algorithms that organise speech patterns into recognisable behaviour. 

    This bold technology implementation in Tanzania could be a useful pilot for overburdened health care systems in Africa hoping to achieve the same benefits.