The term artificial intelligence (AI) has been over-marketed and abused. For instance, a rice cooker with AI, a watch powered by AI, a restaurant ordering app with AI, an air-conditioning unit controlled by AI, a coffee maker run by AI, and more. Practically any product that is controlled by a computer can be made “cool” by adding “AI” as a suffix. In my opinion, most of the products on the market labeled as such are using the term as a marketing gimmick, and do not actually use AI.
In reality, what is artificial intelligence? It is a means of equipping a machine to take on tasks or make decisions in a way that mimic and simulate human intelligence and behavior. Simulating human intelligence and behavior is a difficult thing. It requires lots of data, computational real estate and time to train the machine to display human-like behavior.
A related technique is “machine learning”. This is a part of AI that focuses on algorithms that allow machines to learn and change when exposed to new data without being reprogrammed. Going further, another technique called “deep learning” refers to algorithms that allow a machine to autonomously mimic human thought patterns through artificial neural networks composed of cascading layers of information.
To recap, artificial intelligence is a general term describing a process by which a machine can be equipped with human-like intelligence and behavior, and the other two terminologies refer to methods used to make this happen. Keeping it simple, AI can be thought of as an “intelligent system” mimicking human decision-making ability and behavior.
Tools and Techniques
We now have an idea of what artificial intelligence is. The next question is how one can equip a machine to make a decision that simulates human behavior. There are several tools and techniques used to do this including support vector machines, neural networks, logistic regressions, discriminant analysis, random forest, linear regression, Naïve Bayes Classifiers, nearest neighbor searches, decision trees, Hidden Markov models and others. Support vector machines and neural networks are the techniques used most often, in combination with others.
As an example, I was involved in a project for detecting molecules in the air using a type of odor sensing. We developed a device with 240 sensors that could be used to detect the odor of roasted coffee. For every test, we recorded 240 readings 10 times for a total of 2400 readings. We subjected the device to an additional six varieties of coffee coming from different samples. An algorithm was created to analyze the result using a combination of the techniques mentioned above. The results were probabilistic, identifying the type of coffee with over 90% certainty.
As for the use of AI in medical devices, let us take lung cancer as an example. Developing an early detection tool for lung cancer is very challenging, with most cases being detected at later stage. Using AI as an early detection tool has a strong probability of being a game-changer. Imagine having millions of lung CT scans classified into different stages, ethnicities, types of work, exposures to pollution, and other contributory parameters and conditions, along with an AI engine that can analyze these scans in real-time. According to a study conducted in 2019, a deep learning algorithm achieved state-of-the-art lung cancer detection performance of 94.4%. Using 6,716 cases, the AI outperformed radiologists by 11% in false positives and 5 % in false negatives.
Medical Device Artificial Intelligence Applications
In the medical device space, there are many ways AI can be used in a device or system. Here are 8 applications:
1. Diagnosis of heart diseases
A machine learning algorithm (myocardial-ischemic-injury-index) incorporating age and sex paired with high-sensitivity cardiac troponin I concentrations was used to train an AI platform utilizing data from 3013 patients. The platform was then tested on 7998 patients with suspected myocardial infarction and was found to outperform physicians with a sensitivity of 82.5% and a specificity of 92.2%.
2. Detecting Cancer in Mammography
Breast cancer screening via mammography is a widely accepted tool for breast cancer screening and another area where AI can be applied. With current imaging and analysis tools, cancer cells are often obscured by dense breast tissues. Their appearance on a mammogram can be subtle and can be missed through human error. With the combination of new imaging technologies and an AI engine using a huge set of historical images, the current screening method can be improved by faster analysis, real-time diagnosis, and the absence of human error.
3. Diagnosis of Degenerative Brain Diseases
The diagnosis of neurological conditions such as epilepsy, Alzheimer’s disease, and strokes is a difficult challenge. Current diagnostic technologies (e.g. magnetic resonance imaging, electroencephalogram) produce huge quantities of data for detection, monitoring and treatment of neurological diseases. Analysis of the data tends to be difficult. The use of an intelligent system that accumulate, manage, analyze and automatically detect the neurological abnormalities is crucial. Application of AI in this area will improve the consistency of diagnosis and increase the success of treatment.
4. Detecting a Retinopathy
Diabetic retinopathy (DR) is one of the leading causes of preventable blindness globally. In a study published by American Academy of Opthalmology, a total of 75,137 publicly available fundus images from diabetic patients were used to train and test an artificial intelligence engine to differentiate healthy fundi from those with DR. The results showed an impressive 94% and 98% sensitivity and specificity, respectively.
5. Cell Sorting and Recognizing Cell Types
A study published in Nature demonstrated the use of a neural network in cell sorting. The results showed that the system takes less than a few milliseconds to classify cells and provide a decision to a cell sorter to separate individual target cells in real-time. This study shows the applicability of the AI in classifying white blood cells and epithelial cancer cells with 95.71% sensitivity and 95.74% specificity, label-free.
6. Medical Imaging of Liver
AI is gaining popularity in image-recognition applications. AI using deep learning algorithms can automatically make a quantitative and higher efficiency assessment of the characteristics of complex medical images. One application is in imaging the liver to screen for possible liver diseases using radiology, ultrasound, and nuclear medicine. In the image analysis, AI was used in detecting and evaluating focal liver lesions, facilitating treatment, and predicting the appropriate treatment response.
7. In Vitro Diagnostic Tools
AI can be applied to in vitro diagnostics using real-time imaging to capture fluorescence signals as cells pass through a microfluidic channel. An AI algorithm could be used to differentiate cells by size, shape, and emission wavelength, and can categorize the cells as predictors of certain diseases. Moreover, used in combination with other hardware technologies, this can be done in real-time while maintaining the accuracy of the results. Integrating AI into an in vitro diagnostic platform can improve the performance of the device and diagnostic accuracy.
8. Biosensors for Monitoring Vital Signs
Biosensor-based devices generate huge data sets. Using AI could predict the trends and the probability of disease occurrence. The integration of AI in cardiac monitoring-based biosensors for point of care (POC) diagnostics are a great example. Machine-learning algorithms are used with microchip-based cardiac biosensors for real-time health monitoring and to provide accurate clinical decision in a timely manner.
Final notes
Medical device artificial intelligence will continue to advance and will pave the way for more technological innovations in diagnostics, imaging, mobile computing, and wearables. By incorporating AI, medical devices will become more reliable, accurate and quick in delivering results.
With the exponential increase in the number of published research papers and the growing medical applications, it is evident that regulators are now starting to realize the positive impact of AI in medical device development. Given the increasing investment from the government and private sectors in support of AI as applied to healthcare, more and more companies will be integrating AI into their medical device products.
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Article was written by Lorenzo Gutierrez, who is the StarFish Medical Microfluidics Manager and Interim Toronto Site Director. Lorenzo has extensive experience translating point of care assays to microfluidic cartridges. His microfluidics portfolio includes developing a polyvalence instrument for early infant diagnostics at Chipcare.