Plastic surgery and specialty creep: an analysis of publication trends

Background Many surgical specialties have had pioneering influences from plastic surgeons. However, many of these areas of practice have evolved to include surgeons from diverse training backgrounds. This raises the question as to whether the prominence of other specialties in clinical practice translates to greater research productivity in these areas. The objective of this paper is to investigate the publication volumes of plastic surgeons in selected areas of practice compared to surgeons from other disciplines.Methods PubMed was used to examine publication trends in areas associated with plastic surgery. Searches for the following topics were performed: head and neck reconstruction, hand surgery, breast reconstruction, ventral hernia repair, abdominal component separation, brachial plexus injury, craniofacial surgery, and aesthetic surgery. Affiliation tags were used to examine contributions from nine specialties. Web of Science was used to identify the top cited articles for the last 10 years in each area.Results Articles by non-plastic surgeons comprise the majority of the literature for all areas of practice studied except for breast reconstruction and aesthetic surgery. Despite this, plastic surgeons contributed the greatest number of top cited articles over the last 10 years for five of the areas of practice.Conclusions While plastic surgeons do not contribute the greatest proportion of articles published each year in several of the selected areas of practice, they do publish a larger number of articles that are the most cited. Plastic surgeons remain the dominant academic force in terms of volume and citations for both breast and aesthetic surgery.

Laplace-Beltrami Refined Shape Regression Applied to Neck Reconstruction for Craniosynostosis Patients

This contribution is part of a project concerning the creation of an artificial dataset comprising 3D head scans of craniosynostosis patients for a deep-learning-based classification. To conform to real data, both head and neck are required in the 3D scans. However, during patient recording, the neck is often covered by medical staff. Simply pasting an arbitrary neck leaves large gaps in the 3D mesh. We therefore use a publicly available statistical shape model (SSM) for neck reconstruction. However, most SSMs of the head are constructed using healthy subjects, so the full head reconstruction loses the craniosynostosis-specific head shape. We propose a method to recover the neck while keeping the pathological head shape intact. We propose a Laplace- Beltrami-based refinement step to deform the posterior mean shape of the full head model towards the pathological head. The artificial neck is created using the publicly available Liverpool-York-Model. We apply our method to construct artificial necks for head scans of 50 scaphocephaly patients. Our method reduces mean vertex correspondence error by approximately 1.3 mm compared to the ordinary posterior mean shape, preserves the pathological head shape, and creates a continuous transition between neck and head. The presented method showed good results for reconstructing a plausible neck to craniosynostosis patients. Easily generalized it might also be applicable to other pathological shapes.