Artificial Intelligence–Aided Selection of Needle Pathways: Proof-of-Concept in Percutaneous Lung Biopsies

Abstract

PurposeTo evaluate the concordance between lung biopsy puncture pathways determined by artificial intelligence (AI) and those determined by expert physicians. Materials and MethodsAn AI algorithm was created to choose optimal lung biopsy pathways based on segmented thoracic anatomy and emphysema in volumetric lung computed tomography (CT) scans combined with rules derived from the medical literature. The algorithm was validated using pathways generated from CT scans of randomly selected patients (n = 48) who had received percutaneous lung biopsies and had noncontrast CT scans of 1.25-mm thickness available in picture archiving and communication system (PACS) (n = 28, mean age, 68.4 years ± 9.2; 12 women, 16 men). The algorithm generated 5 potential pathways per scan, including the computer-selected best pathway and 4 random pathways (n = 140). Four experienced physicians rated each pathway on a 1–5 scale, where scores of 1–3 were considered safe and 4–5 were considered unsafe. Concordance between computer and physician ratings was assessed using Cohen’s κ. ResultsThe algorithm ratings were statistically equivalent to the physician ratings (safe vs unsafe: κ¯=0.73; ordinal scale: κ¯=0.62). The computer and physician ratings were identical in 57.9% (81/140) of cases and differed by a median of 0 points. All least-cost “best” pathways generated by the algorithm were considered safe by both computer and physicians (28/28) and were judged by physicians to be ideal or near ideal. ConclusionsAI-generated lung biopsy puncture paths were concordant with expert physician reviewers and considered safe. A prospective comparison between computer- and physician-selected puncture paths appears indicated in addition to expansion to other anatomic locations and procedures.