An automated parametric ear model to improve frugal 3D scanning methods for the advanced manufacturing of high-quality prosthetic ears.

Ear prostheses are commonly used for restoring aesthetics to those suffering missing or malformed external ears. Traditional fabrication of these prostheses is labour intensive and requires expert skill from a prosthetist. Advanced manufacturing including 3D scanning, modelling and 3D printing has the potential to improve this process, although more work is required before it is ready for routine clinical use. In this paper, we introduce a parametric modelling technique capable of producing high quality 3D models of the human ear from low-fidelity, frugal, patient scans; significantly reducing time, complexity and cost. Our ear model can be tuned to fit the frugal low-fidelity 3D scan through; (a) manual tuning, or (b) our automated particle filter approach. This potentially enables low-cost smartphone photogrammetry-based 3D scanning for high quality personalised 3D printed ear prosthesis. In comparison to standard photogrammetry, our parametric model improves completeness, from (81 ± 5)% to (87 ± 4)%, with only a modest reduction in accuracy, with root mean square error (RMSE) increasing from (1.0 ± 0.2) mm to (1.5 ± 0.2) mm (relative to metrology rated reference 3D scans, n = 14). Despite this reduction in the RMS accuracy, our parametric model improves the overall quality, realism, and smoothness. Our automated particle filter method differs only modestly compared to manual adjustments. Overall, our parametric ear model can significantly improve quality, smoothness and completeness of 3D models produced from 30-photograph photogrammetry. This enables frugal high-quality 3D ear models to be produced for use in the advanced manufacturing of ear prostheses.

Capturing patient anatomy for designing and manufacturing personalized prostheses.

Prostheses play a critical role in healthcare provision for many patients and encompass aesthetic facial prostheses, prosthetic limbs and prosthetic joints, bones, and other implantable medical devices in musculoskeletal surgery. An increasingly important component in cutting-edge healthcare treatments is the ability to accurately capture patient anatomy in order to guide the manufacture of personalized prostheses. This article examines methods for capturing patient anatomy and discusses the degrees of personalization in medical manufacturing alongside a summary of current trends in scanning technology with a focus on identifying workflows for incorporating personalization into patient-specific products. Over the next decade, with increased harmonization of both personalization and automated prosthetic manufacturing will be the realization of improved patient compliance, satisfaction, and clinical outcomes.

Frugal 3D scanning using smartphones provides an accessible framework for capturing the external ear.

Three-dimensional (3D) scanning technologies, such as medical imaging and surface scanning, have important applications for capturing patient anatomy to create personalised prosthetics. Digital approaches for capturing anatomical detail as opposed to traditional, invasive impression techniques significantly reduces turnaround times and lower production costs while still maintaining the high aesthetic quality of the end product. While previous case studies utilise expensive 3D scanning and modelling frameworks, their clinical translation is limited due to high equipment costs. In this study, we develop and validate a low-cost framework for clinical 3D scanning of the external ear using photogrammetry and a smartphone camera. We recruited five novice operators who watched an instructional video before scanning 20 healthy adult participant ears who did not have microtia. Our results show that the smartphone-based photogrammetry methodology produces 3D scans of the external ear that were accurate to (1.5 ± 0.4) mm and were (71 ± 14) % complete compared with those from a gold standard reference scanner, with no significant difference observed between operators. A moderate to strong interrater reliability was determined for all novice operators, suggesting that all novice operators were able to capture repeatable scans. The development of this smartphone photogrammetry approach has the potential to provide a non-invasive, inexpensive and accessible means to capture patient morphology for use in clinical assessment and personalised device manufacture, specifically for ear prostheses. We also demonstrate that inexperienced operators can rapidly learn and apply smartphone photogrammetry for accurate and reliable scans of the external ear with important applications for future clinical translation.

A Method for Economical Smartphone-Based Clinical 3D Facial Scanning.

PURPOSE: Three-dimensional (3D) facial scanning is an emerging clinical tool to capture external anatomical features for quantitative assessment and treatment in a wide range of clinical settings. MATERIALS AND METHODS: In this study, an economical approach for rapid scanning of faces in the clinic was developed and validated to record valuable 3D patient data using smartphone cameras and photogrammetry software. Five novice operators were recruited to watch an instructional video developed on the technique before scanning 20 healthy adult participants. RESULTS: The smartphone-based photogrammetry approach produced scans with 1.3 mm (±0.3 mm) accuracy in comparison to a metrology-rated gold standard device and were 88% (±14%) complete, with no significant difference observed between operators. A moderate to strong intrarater reliability was determined for all novice operators, suggesting that first-use operators can capture consistent scans based on watching an instructional video. CONCLUSION: Smartphone photogrammetry could provide a rapid, noninvasive and economical method to capture patient morphological data for clinical assessment and personalized device manufacture. Inexperienced operators can quickly learn and utilize smartphone photogrammetry to provide accurate and reliable facial scans, essential for future clinical translation.