Bratt, Alexander K.

Pulmonary developmental disorders comprise a heterogeneous group of congenital abnormalities involving the airways, lung parenchyma, pulmonary vasculature, or overlapping developmental pathways. Although these conditions commonly present during infancy or childhood, many remain clinically silent until adulthood, when they are discovered incidentally or manifest with recurrent respiratory symptoms such as infection, hemoptysis, or progressive dyspnea. These abnormalities may be broadly categorized into airway, vascular, parenchymal, and mixed developmental anomalies. Airway abnormalities include congenital tracheobronchomegaly, branching anomalies of the tracheobronchial tree, bronchial atresia, bronchogenic cysts, congenital pulmonary airway malformations (CPAM), and bronchopulmonary sequestration (BPS). Vascular anomalies include pulmonary artery sling, proximal interruption of the pulmonary artery, Scimitar syndrome, and meandering pulmonary veins. Parenchymal disorders encompass congenital lobar overinflation and pulmonary hypoplasia. Increasing recognition of overlapping lesions, particularly combinations of CPAM, sequestration, and bronchial atresia, has challenged traditional compartmental classification systems and supports a developmental spectrum model. This review synthesizes contemporary concepts regarding embryologic mechanisms, pathogenesis, imaging findings, and management of these entities in adults. Particular emphasis is placed on imaging evaluation with CT and MRI, updated terminology, evolving molecular understanding of CPAMs, including lesions with malignant potential, and distinctions between congenital sequestration and pseudosequestration. Recognition of these disorders and their associated anomalies is essential for accurate diagnosis, appropriate management, and surgical planning in adult patients.

Background: There is intense interest and speculation in the application of artificial intelligence (AI) to radiology. The goals of this investigation were (1) to assess thoracic radiologists’ perspectives on the role and expected impact of AI in radiology, and (2) to compare radiologists’ perspectives with those of computer science (CS) experts working in the AI development. Methods: An online survey was developed and distributed to chest radiologists and CS experts at leading academic centers and societies, comparing their expectations of AI’s influence on radiologists’ jobs, job satisfaction, salary, and role in society. Results: A total of 95 radiologists and 45 computer scientists responded. Computer scientists reported having read more scientific journal articles on AI/machine learning in the past year than radiologists (mean [95% confidence interval]=17.1 [9.01-25.2] vs. 7.3 [4.7-9.9], P=0.0047). The impact of AI in radiology is expected to be high, with 57.8% and 73.3% of computer scientists and 31.6% and 61.1% of chest radiologists predicting radiologists’ job will be dramatically different in 5 to 10 years, and 10 to 20 years, respectively. Although very few practitioners in both fields expect radiologists to become obsolete, with 0% expecting radiologist obsolescence in 5 years, in the long run, significantly more computer scientists (15.6%) predict radiologist obsolescence in 10 to 20 years, as compared with 3.2% of radiologists reporting the same (P=0.0128). Overall, both chest radiologists and computer scientists are optimistic about the future of AI in radiology, with large majorities expecting radiologists’ job satisfaction to increase or stay the same (89.5% of radiologists vs. 86.7% of CS experts, P=0.7767), radiologists’ salaries to increase or stay the same (83.2% of radiologists vs. 73.4% of CS experts, P=0.1827), and the role of radiologists in society to improve or stay the same (88.4% vs. 86.7%, P=0.7857). Conclusions: Thoracic radiologists and CS experts are generally positive on the impact of AI in radiology. However, a larger percentage, but still small minority, of computer scientists predict radiologist obsolescence in 10 to 20 years. As the future of AI in radiology unfolds, this study presents a historical timestamp of which group of experts’ perceptions were closer to eventual reality.