A Pilot Clinical Study of Ocular Prosthesis Fabricated by Three-dimensional Printing and Sublimation Technique

Purpose: We sought to evaluate the safety and effectiveness of patient-specific ocular prostheses produced by three-dimensional (3D) printing and the sublimation technique. A comparison with prostheses produced using manual manufacturing methods was then performed. Methods: To confirm the biological and physiochemical safety, cytotoxicity, systemic acute toxicity, intradermal reaction, and skin sensitization tests were conducted according to the International Organization for Standardization guidelines. The compressive strength of the prostheses was also tested. Further, a case series of three patients who wore the 3D printed prostheses for more than eight hours daily for 4 weeks was executed. Self-assessments by these individuals using a questionnaire and safety evaluations focusing on the occurrence of conjunctival inflammation or allergic reactions according to the Cornea and Contact Lens Research Unit criteria by slit-lamp examination and similarity assessment were completed. Results: The 3D printed ocular prostheses met the necessary qualifications per the biological and physiochemical safety tests, showing the absence of cytotoxicity, acute systemic toxicity, intradermal reactivity, and skin-sensitizing potency. Also, there was no difference in strength test results between previous ocular prostheses and the 3D printed ones. Self-assessment by the patients yielded satisfactory results, with no significant difference in the level of satisfaction reported for the 3D printed and previous handmade ocular prostheses. The 3D printed prosthesis did not trigger any side effects in the conjunctival sac and showed similar objective findings with respect to the color of the iris, sclera, and vessel patterns. Conclusions: Our study confirms the biologic and physiochemical safety of 3D-printed ocular prostheses created using computer-aided design technology and a sublimation technique. The patients' questionnaires and the judgment of the ophthalmologists/ocularists showed that the 3D printed ocular prosthesis was acceptable in function and appearance through a case series report.ope

Applying machine learning methods to enable automatic customisation of knee replacement implants from CT data

The aim of this study was to develop an automated pipeline capable of designing custom total knee replacement implants from CT scans. The developed pipeline firstly utilised a series of machine learning methods including classification, object detection, and image segmentation models, to extract geometrical information from inputted DICOM files. Statistical shape models then used the information to create femur and tibia 3D surface model predictions which were ultimately used by computer aided design scripts to generate customised implant designs. The developed pipeline was trained and tested using CT scan images, along with segmented 3D models, obtained for 98 Korean Asian subjects. The performance of the pipeline was tested computationally by virtually fitting outputted implant designs with ‘ground truth’ 3D models for each test subject’s bones. This demonstrated the pipeline was capable of repeatably producing highly accurate designs, and its performance was not impacted by subject sex, height, age, or knee side. In conclusion, a robust, accurate and automatic, CT-based total knee replacement customisation pipeline was shown to be feasible and could afford significant time and cost advantages over conventional methods. The pipeline framework could also be adapted to enable customisation of other medical implants

Applied AI/ML for automatic customisation of medical implants

Most knee replacement surgeries are performed using ‘off-the-shelf’ implants, supplied with a set number of standardised sizes. X-rays are taken during pre-operative assessment and used by clinicians to estimate the best options for patients. Manual templating and implant size selection have, however, been shown to be inaccurate, and frequently the generically shaped products do not adequately fit patients’ unique anatomies. Furthermore, off-the-shelf implants are typically made from solid metal and do not exhibit mechanical properties like the native bone. Consequently, the combination of these factors often leads to poor outcomes for patients. Various solutions have been outlined in the literature for customising the size, shape, and stiffness of implants for the specific needs of individuals. Such designs can be fabricated via additive manufacturing which enables bespoke and intricate geometries to be produced in biocompatible materials. Despite this, all customisation solutions identified required some level of manual input to segment image files, identify anatomical features, and/or drive design software. These tasks are time consuming, expensive, and require trained resource. Almost all currently available solutions also require CT imaging, which adds further expense, incurs high levels of potentially harmful radiation, and is not as commonly accessible as X-ray imaging. This thesis explores how various levels of knee replacement customisation can be completed automatically by applying artificial intelligence, machine learning and statistical methods. The principal output is a software application, believed to be the first true ‘mass-customisation’ solution. The software is compatible with both 2D X-ray and 3D CT data and enables fully automatic and accurate implant size prediction, shape customisation and stiffness matching. It is therefore seen to address the key limitations associated with current implant customisation solutions and will hopefully enable the benefits of customisation to be more widely accessible.Open Acces