Clinicians pivoted their AI efforts to engage in the battle against the COVID-19 pandemic. How can it help those with persistent symptoms – the so-called “long COVID”?
The emergence in March 2020 of the COVID-19 virus as a pandemic had a profound effect on the demand for health services that continues to this day. The impact on laboratory services was particularly notable in the early days of the pandemic. A study helmed by Thomas J S Durant of the Department of Laboratory Medicine at the Yale University School of Medicine found that from late February to mid-April 2020, more than 870,000 COVID-19 tests were administered in the U.S., but overall lab testing went down significantly. Quickly, COVID had become a burden that was affecting other laboratory functions.
But some hospital laboratories had an ace up their sleeves: Artificial intelligence (AI). Some developed algorithms to predict the likelihood of a patient contracting COVID based on demographic data and vaccination history to prioritize then-limited testing resources. Some adapted existing projects to predict respiratory failure.
Some retrained radiological imaging AI projects were created to speed the diagnosis of thoracic ailments. Others used AI and machine learning (ML) modeling to find which patients needed less attentive care and those likely to require intubation (the procedure that’s used when you can’t breathe on your own). Massachusetts Institute of Technology (MIT) researchers also developed an AI model that could distinguish even asymptomatic COVID-19 sufferers with startling accuracy by analyzing recordings of coughs collected over mobile phones. The model was adapted from algorithms already proven to detect asthma and pneumonia accurately.
In short, AI and ML have eased healthcare burdens by de-escalating patients to free up resources, prioritizing testing for those most likely to have been exposed, and providing diagnostics with novel forms of data capture. While this benefits the predictive and early treatment phases of the COVID protocol, AI must also play a role in post-acute cases.
While most people recover from a COVID infection within a few weeks, some have symptoms that linger. Long COVID – or post-acute COVID, or chronic COVID – is defined as symptoms lingering for 12 weeks or more after infection that can’t be explained by another existing or recently acquired condition. The list of symptoms is long: trouble breathing, headaches, fever, “brain fog,” heart palpitations, joint or muscle pain, and changes in smell or taste are just a few. Long COVID can result from the initial infection’s damage to the lungs, the heart, kidneys, skin, and brain – practically any organ in the human body. And Long COVID isn’t just a problem for those who were severely ill or hospitalized because of COVID; it can manifest in COVID patients who were asymptomatic during the acute phase of the infection.
We can leverage the front-line AI and ML technologies to help manage long COVID suffering. Predictive models could ascertain the likelihood a patient could suffer from post-acute COVID, determine which patients are suffering from non-infectious consequences of COVID, like isolation and loss of income, and comb through the chemical composition of an entire library of pharmaceutical treatments that may be effective at treating post-COVID symptoms.
We have an enormous body of data to train artificial intelligence and machine learning platforms. But there is an impediment to its success: siloing.
When COVID-19 was first declared a pandemic, health care providers had to scramble to adopt or adapt AI tools to address their specific challenges. This meant that modeling and algorithm development took place in-house rather than in a shared forum. This leads to no guarantee of consistent data collection or output format that could be shared with other providers, even within the same region, where shared results would be most beneficial.
Interoperation of disparate systems is a vital component of a comprehensive approach to predicting, detecting, and treating COVID-19 and its post-acute complications. This requires standardization of reporting formats on a healthcare authority or statewide basis, ideally compliant with Centers for Disease Control (CDC) protocols. Data collection procedures must be formalized, digitized in a form that is resistant to error, and brought as close to the patient as possible for maximum accuracy.
AI and ML technologies offer nearly boundless opportunities in the life sciences field—modeling potential disease hot spots, discerning the desperately ill who require attentive treatment from those with a mild illness that can be self-managed, and predicting what resources will be needed and where to battle the pandemic. We have a head start on fighting Long COVID and the related symptoms by preparing for interoperability.
