In-silico to replace in-vivo in clinical trials?
When it comes to clinical trials, patient recruitment is laborious, ethically contentious, medically risky and can be expensive too. But what if there was a safer and more economical way to test drugs?
With the advancement of AI, machine learning and bioinformatics, it becomes possible to simulate real biological processes in virtual settings through the use of big data. In-silico is the term scientists are using to describe the modelling, simulation, and visualisation of biological and medical processes in computers.
The main advantage of in-silico trials is the ability to trial the effects of new drugs or treatment options in a virtual setting without real consequences for either animals or humans. It’s also an enhancement for personalised medicine where it can be used by doctors to try out treatment plans, to get to know the behaviour of drugs or to identify the most appropriate drug dosage.
While completely simulated clinical trials are not yet feasible with the current technology, its development would be expected to have major benefits over current in vivo clinical trials. The FDA is already planning for a future in which more than half of all clinical trial data will come from computer simulations.
- 605 views
- August 29, 2019
- Ameera Hamid
Medical imaging’s the big gainer from AI and ML
AI’s not new. It emerged in the 1960s. A blog from PLOS Speaking of Medicine says advertising hyperbole has led to scepticism and misunderstanding of what’s possible with machine learning (ML) and what’s not with. The blog sets about providing an accessible, scientifically and technologically accurate portrayal of ML’s current state in clinical translation.
Medical imaging workflows are seen as benefiting most in the short-term. ML algorithms automatically processing two or three-dimensional scans to identify clinical signs of conditions, such as tumours and lesions, and determining likely diagnoses have been published. Some are progressing through regulatory steps toward the market.
Many use deep learning. It’s a form of ML based on layered representations of variables, ML’s neural networks. It’s benefited ophthalmology. A major UK eye hospital has used deep learning to deal with a clinically-heterogeneous set of three dimensional optical Coherence Tomography (CT) scans. Referral recommendations reached or exceeded experts’ decisions.
Radiologic diagnoses are another ML beneficiary. An algorithm detected 14 clinically important pathologies from frontal-view chest radiographs. They included:PneumoniaPleural effusionPulmonary masses and nodules.
ML’s performance matched practicing radiologists. Another
There’s several other clinical activities where ML can benefit healthcare. They include:Triage and preventionClustering for discovery of disease sub-typesAnomaly detection to reduce medication errorsAugmented doctors.
The blog’s an advance report. Its final version’ll be in PLOS Medicine at the end of December. It’s a valuable guide for Africa’s health systems’ eHealth strategies. An initial step’s to lay down data foundations.
- 677 views
- December 05, 2018
- Ameera Hamid
Robots could be good for your health
In his book The Rise of the Robots, published by One World, Martin Ford proposes social and economic scenarios for robots that are good for output, but not so good for people. He sees significant upheaval and displacement from employment across a wide range of commercial and industrial activities and across middle and low income families. The drop in income, so spending power, will degrade economies.
Simultaneously, robots aren’t paid and don’t spend money. He sees this as exacerbating the social and economic impact.
Healthcare’s the activity that’s different. He sees the robots marching into healthcare that’s already over-stretched as needs and demands continuously outstrip supply. Four roles are crucial:Artificial intelligence in medicineHospital and pharmacy roboticsRobots that care for the elderlyUnleashing the power of data.
For low and middle income countries and health systems, sustained investment in robots could be part of the solution. They can improve healthcare professionals’ productivity and help to meet demand.
They should find a place in Africa’s eHealth strategies. Small scale investment will lay out a trajectory for the future.
- 466 views
- October 17, 2018
- Ameera Hamid
AI and machine learning need data storage resources
Many things come in bundles. Amit Ray, author of Mindfulness Meditation for Corporate Leadership and Management says “As more and more artificial intelligence is entering into the world, more and more emotional intelligence must enter into leadership.” It’s not enough. A report by Source Media, sponsored by Pure Storage says powerful, advanced computing and storage capacity and capabilities are needed too.
It recognises AI’s “vast” potential. Currently, some radiology departments use it effectively to improve workloads. Progress across other clinical activities depends on extra computing and storage power for two activities, training and clinical use.
When researchers deliver AI and machine learning techniques to clinical practice and healthcare, solutions need huge amounts of data for training models, including labelling data. It’s especially important for neural networks. These are hardware and software patterned on the way neurons work in human brains. They’re deep learning technologies often focusing on solving complex signal processing or pattern recognition problems.
If storage’s inadequate, it can’t keep up with the workload. The result’s diminished AI. Healthcare’s typical eHealth investment model’s to buy enough computing storage infrastructure as a minimum requirement, then expand it a few years after it’s clogged up. Eventually, it’s replaced with modern solutions after a period of obsolescence.
This doesn’t fit AI and machine learning. It has to match the computer power and storage capacity needed as AI and machine learning expands. Developers and healthcare organisations can then move beyond exploring AI’s potential and bring into full use. The, patients benefit.While assembling the resources needed for AI and machine learning’s challenging for Africa’ health systems, the infrastructure requirements add to the constraints. Before venturing into the AI space, it’s essential to contemplate and deal with the whole resource requirements and their affordability.
- 845 views
- June 15, 2018
- Tom Jones
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.
- 541 views
- April 06, 2018
- Ameera Hamid
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
- 723 views
- March 05, 2018
- Ameera Hamid
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.
- 505 views
- February 28, 2018
- Ameera Hamid
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