Background In the production of ankle-foot orthoses and in-shoe foot orthoses, lower leg morphology is traditionally captured using a plaster cast or foam impression box. Plaster-based processes are a time-consuming and labour-intensive fabrication method. 3D scanning is a promising alternative, however how these new technologies compare with traditional methods is unclear. The aim of this systematic review was to compare the speed, accuracy and reliability of 3D scanning with traditional methods of capturing foot and ankle morphology for fabricating orthoses. Methods PRISMA guidelines were followed and electronic databases were searched to March 2020 using keywords related to 3D scanning technologies and traditional foot and ankle morphology capture methods. Studies of any design from healthy or clinical populations of any age and gender were eligible for inclusion. Studies must have compared 3D scanning to another form of capturing morphology of the foot and/or ankle. Data relating to speed, accuracy and reliability as well as study design, 3D scanner specifications and comparative capture techniques were extracted by two authors (M.F. and Z.W.). Study quality was assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) and Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN). Results Six articles met the inclusion criteria, whereby 3D scanning was compared to five traditional methods (plaster cast, foam impression box, ink footprint, digital footprint and clinical assessment). The quality of study outcomes was rated low to moderate (GRADE) and doubtful to adequate (COSMIN). Compared to traditional methods, 3D scanning appeared to be faster than casting (2 to 11 min vs 11 to 16 min). Inter-rater reliability (ICC 0.18–0.99) and intra-rater reliability (ICCs 0.25–0.99) were highly variable for both 3D scanning and traditional techniques, with higher agreement generally dependent on the foot parameter measured. Conclusions The quality and quantity of literature comparing the speed, accuracy and reliability of 3D scanning with traditional methods of capturing foot and ankle morphology is low. 3D scanning appears to be faster especially for experienced users, however accuracy and reliability between methods is variable.

Ankle–foot orthoses (AFOs) are devices prescribed to improve mobility in people with neuromuscular disorders. Traditionally, AFOs are manually fabricated by an orthotist based on a plaster impression of the lower leg which is modified to correct for impairments. This study aimed to digitally analyse this manual modification process, an important first step in understanding the craftsmanship of AFO fabrication to inform the digital workflows (i.e. 3D scanning and 3D printing), as viable alternatives for AFO fabrication. Pre- and post-modified lower limb plaster casts of 50 children aged 1–18 years from a single orthotist were 3D scanned and registered. The Euclidean distance between the pre- and post-modified plaster casts was calculated, and relationships with participant characteristics (age, height, AFO type, and diagnosis) were analysed. Modification maps demonstrated that participant-specific modifications were combined with universally applied modifications on the cast's anterior and plantar surfaces. Positive differences (additions) ranged 2.12–3.81 mm, negative differences (subtractions) ranged 0.76–3.60 mm, with mean differences ranging from 1.37 to 3.12 mm. Height had a medium effect on plaster additions (rs = 0.35). We quantified the manual plaster modification process and demonstrated a reliable method to map and compare pre- and post-modified casts used to fabricate children's AFOs.

Background Predicting morphological changes to anatomical structures from 3D shapes such as blood vessels or appearance of the face is a growing interest to clinicians. Machine learning (ML) has had great success driving predictions in 2D, however, methods suitable for 3D shapes are unclear and the use cases unknown. Objective and methods This systematic review aims to identify the clinical implementation of 3D shape prediction and ML workflows. Ovid-MEDLINE, Embase, Scopus and Web of Science were searched until 28th March 2022. Results 13,754 articles were identified, with 12 studies meeting final inclusion criteria. These studies involved prediction of the face, head, aorta, forearm, and breast, with most aiming to visualize shape changes after surgical interventions. ML algorithms identified were regressions (67%), artificial neural networks (25%), and principal component analysis (8%). Meta-analysis was not feasible due to the heterogeneity of the outcomes. Conclusion 3D shape prediction is a nascent but growing area of research in medicine. This review revealed the feasibility of predicting 3D shapes using ML clinically, which could play an important role for clinician-patient visualization and communication. However, all studies were early phase and there were inconsistent language and reporting. Future work could develop guidelines for publication and promote open sharing of source code.