AI is rapidly transforming the landscape of eye care. This article discusses in greater detail the major areas in myopia management where AI is likely* to play a significant role in the near future.
Early detection and progression
Machine learning algorithms can analyze data points, including genetic factors, environmental influences, and ocular measurements, such as keratometry and axial length, to identify individuals at high risk for developing myopia. These algorithms can process large datasets, aiding in the identification of subtle patterns and correlations that may not be apparent to human clinicians.
Additionally, many variables, such as behavioral patterns or fundus appearance, that can be challenging to define or quantify, are analyzed with great efficiency and sensitivity by deep-learning models, aiding in the monitoring of disease progression.
Streaming analysis/recommendations
Deep-learning models provide a streaming analysis of real-world patient data, offering insights that can be more generalizable to the broader patient population vs. data derived from randomized controlled trials (RCTs). RCTs, while valuable, often have strict inclusion criteria that may not represent the diversity of real-world patients. Examples of commonly seen clinical cases not represented in RCTs include young age, anisometropia, strabismus, preexisting ocular conditions, and, most importantly, a history of previous treatment.
Deep-learning algorithms can analyze vast amounts of data from EHR, wearable devices, and patient-reported outcomes to identify trends, patterns, and correlations that are more reflective of everyday clinical practice vs. the data captured by RCTs. Such algorithms, in turn, aid in management decisions.
Monitoring using wearable devices
Wearable devices can continuously track environmental and biometric parameters, such as ambient light levels, viewing distance, heart rate, blood pressure, etc. This tracking provides real-time data on many factors that influence myopia management effectiveness.
Additionally, wearable devices can monitor patient adherence to treatment and detect adverse reactions or complications early.
Individualized patient education
AI-driven educational platforms can provide personalized content, including videos, articles, and interactive tools, that ensure patients are well-informed about their condition, treatment options, and the importance of adherence to prescribed regimens.
Virtual consultation/in-person visits
Virtual consultations powered by AI can analyze patient data and aid in preliminary diagnoses and in recommending appropriate interventions, reducing the need for in-person visits, while also making care more accessible in underserved areas.
Also, AI can assist in the monitoring of patients through telemedicine platforms, ensuring that treatment plans are effectively implemented and adjusted as needed.
Numerous benefits
As discussed above, the integration of AI in myopia management offers numerous benefits. As AI technology continues to advance, its role in myopia management is expected to expand, leading to improved patient outcomes and enhanced quality of care. OM
