A proof-of-concept study suggests that artificial intelligence (AI) may classify images captured during rapid onsite examination of endobronchial ultrasound guided transbronchial need aspiration (EBUS-TBNA) with high accuracy. The results of this study were published in the European Respiratory Journal. The use of AI in medicine has become more common in areas such as cervical cancer screening, which has led experts to question its potential in other fields of medicine. No data have been published on the application of AI during rapid on-site examination of EBUS-TBNA. A team of investigators "evaluated the performance of an AI model, consisting of an open-sounded convolutional neural network using transfer learning, for its ability to accurately classify images of [rapid onsite examination] of EBUS-TBNA smears in the bronchoscopy suite."

Machine learning modules can be trained with the use of electronic health record (EHR) data to differentiate between transient and persistent cases of early childhood asthma, according the results of an analysis published in PLoS One. Researchers conducted a retrospective cohort study using data derived from the Pediatric Big Data (PBD) resource at the Children's Hospital of Philadelphia (CHOP) -- a pediatric tertiary academic medical center located in Pennsylvania. The researchers sought to develop machine learning modules that could be used to identify individuals who were diagnosed with asthma at aged 5 years or younger whose symptoms will continue to persist and who will thus continue to experience asthma-related visits. They trained 5 machine learning modules to distinguish between individuals without any subsequent asthma-related visits (transient asthma diagnosis) from those who did experience asthma-related visits from 5 to 10 years of age (persistent asthma diagnosis), based on clinical information available in these children up to 5 years of age. The PBD resource used in the current study included data obtained from the CHOP Care Network -- a primary care network of more than 30 sites -- and from CHOP Specialty Care and Surgical Centers.

Use of a deep-learning convolutional neural network (CNN) -- a form of artificial intelligence -- can help reveal patterns on chest computed tomography (CT) scans that identify smokers at high long-term risk for lung cancer well beyond the Centers for Medicare & Medicaid Services (CMS) criteria for lung screening eligibility, according to the results of an analysis published in the Annals of Internal Medicine. Investigators sought to create and validate a CNN -- that is, the CXR-LC model -- with the ability to predict long-term incident lung cancer via the use of data typically available in a patient's electronic medical record, including chest radiographs, sex, age, and current smoking status. The CXR-LC model was developed in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial, which included to total of 41,856 patients. The final CXR-LC model was validated in additional smokers from the PLCO study (n 5615; 12-year follow-up) and National Lung Screening Trial (NLST) heavy smokers (n 5493; 6-year follow-up). There were more current smokers (50.4% vs 20.2%, respectively) and higher mean pack-years (55.7 vs 35.4,

The advent of large data sets from many sources (big data), machine learning, and artificial intelligence (AI) are poised to revolutionize asthma care on both the investigative and clinical levels, according to an article published in the Journal of Allergy and Clinical Immunology. During 15-minute clinic visits, only a short amount of time is spent understanding and treating what is a complex disease, and only a fraction of the necessary data is captured in the electronic health record. "Our patients and the pace of data growth are compelling us to incorporate insights from Big Data to inform care," the researchers posit. "Predictive analytics, using machine learning and artificial intelligence has revolutionized many industries," including the healthcare industry. When used effectively, big data, in conjunction with electronic health record data, can transform the patient's healthcare experience.