Abstract Hirschsprung disease is a congenital defect that affects an estimated 1:5000 liveborn infants. It is characterized by absent ganglion cells (neurons) in the distal rectum and a variable length of contiguous proximal bowel. The diagnosis of Hirschsprung disease relies upon subjective pathologist interpretation of up to 100 tissue sections from suction rectal biopsies to prove the absence of ganglion cells and the presence of hypertrophic nerves. Due to the labor-intensive and time-consuming nature of the current diagnostic process, there is a need to more efficiently and comprehensively diagnose Hirschsprung disease. Our team has pioneered the use of 3D open-top light-sheet (OTLS) microscopy, which enables rapid, high-throughput imaging of large clinical samples. In combination with cutting-edge machine learning techniques, we hypothesize that 3D OTLS microscopy can provide more accurate and consistent assessment of ganglion cells and hypertrophic nerves in suction rectal biopsies from patients with suspected Hirschsprung disease. We will test this hypothesis by developing a multiplex staining protocol and machine learning analysis pipeline, which will be piloted on 100 rectal biopsy specimens from fresh resection specimens.