Diagnosis of allergic diseases
The diagnosis or classification of allergic disease has been the area in which AI has been applied most, an exemplary case of supervised learning105–109. ML has used a wide range of data sources to improve allergy or asthma diagnosis: text data from electronic health records (EHRs), sound data of wheezes,image data from lung CT scans, or large-scale multi-omicsdata. The extraction of relevant clinical features from EHRs using NLP has successfully diagnosed (childhood) asthma in discovery and replication cohorts. In a study of a US birth cohort study, Seolet al. (2020) applied an AI algorithm to define asthma using established predictive and diagnostic criteria in 8196 children. Of all patients that met those criteria, 30% did not have a physician diagnosis of asthma, signifying the potential for early disease identification and population management with EHRs.
Additionally, several studies have investigated the potential of omics data for diagnosis. One study developed an ML model that diagnosed IgE-sensitized allergic disease in 16-year-old children based on nasal cell DNA-methylation of only three CpG sites. External validation in an independent cohort indicated the prospect of reproducible epigenetic tests for diagnosis. Alag et al. (2019) pursued a similar approach to diagnosing food allergy, where neural networks were trained on blood epigenetic markers. The predictive markers were subsequently associated with a 13-gene profile linked to immune response. This study highlights the potential of novel diagnostic approaches to food allergy.
ML-based modelling of the component-resolved diagnostic multiplex array data has shown that component-specific IgE responses to multiple allergenic proteins are functionally coordinated and co-regulated, and that the networks of interactions are associated with asthma diagnosis and severity. Machine learning has also been used to predict disease risk or persistence. In a prospective study of 704 children aged 2 to 13 months, unsupervised clustering on 16S rRNA data was used to identify profiles of longitudinal changes in nasal airway microbiota that were significantly associated with asthma risk at age seven. These results affirm that the microbiome plays a vital role in the early development of asthma and show promise for early identification and prevention strategies. In another study, a supervised machine learning model was able to predict asthma persistence in almost 10,000 patients diagnosed before age 5 for persistence by age 10. The XGBoost algorithm delivered the most robust performance (AUC=0.86), using clinically relevant features such as the number of (non) asthma-related visits before age five and noninvasive pulse oximetry data. The study was not independently replicated, which is essential in pursuing clinical support tools. Kothalawala et al. (2021) used data from birth cohorts to train and validate two predictive models, CAPE and CAPP, to predict the likelihood of asthma at school-age using predictors from 0-2 and 0-4 years of age, respectively. Predictive performance was externally validated in the Manchester Asthma and Allergy Study (MAAS) cohort. Support Vector Machine (SVM) algorithms provided the best performance for both the CAPE (AUC=0.71) and CAPP (AUC=0.82) models, and both demonstrated good generalisability in the replication cohort, performing better than previous regression-based models.
AI guided image analysis has been performed to diagnose eczema. One study developed a classifier of atopic dermatitis in multiphoton tomography images, reaching over 97% accuracy through transfer learning. Highlighted areas of interest in the images could support clinicians in faster diagnosis.
Prediction of asthma exacerbations and hospitalizationsAsthma exacerbations are related to increased morbidity, mortality, and healthcare use, yet these are challenging to predict. Several studies have applied ML to predict exacerbations. In a large study involving EHR data from 60.000 patients, researchers used different ML techniques in a supervised setup to predict three exacerbation outcomes: oral glucocorticoid bursts, ED visits, and hospitalization. The study achieved a ROC AUC of 0.88 on the latter outcome, which is significantly higher than the results of previous studies (AUC of 0.77); this was replicated in an independent cohort. Important predictors for hospitalization included oral glucocorticoid burst, inhaled corticosteroid, and blood creatinine, the latter being unexpected. Another study used self-reported daily home monitoring data of asthma symptoms and peak expiratory flow, which were reduced in dimensionality using PCA and then fed to various supervised ML methods. The best model achieved a sensitivity of 90% and specificity of 83%, predicting severe asthma exacerbations on the same day or up to three days in the future. A more extensively validated example is Asthma-Guidance and Prediction System (a-GPS), an AI tool to optimize asthma management. A-GPS uses NLP on open text from EHRs to provide clinicians with the most relevant clinical information. In a randomized control trial, the tool significantly reduced the time for reviewing EHRs (11.3 to 3.5 min), but no significant change in clinical outcome (i.e., exacerbations) was observed. Sensor data from an electronic multi-dose dry powder inhaler (eMDPI), such as inhalation volume and duration, has also been utilized to predict exacerbations with a ROC AUC of 0.83.