Emissivity prediction of functionalized surfaces using artificial intelligence

Tuning surface emissivity has been of great interest in thermal radiation applications, such as thermophotovoltaics and passive radiative cooling. As a low-cost and scalable technique for manufacturing surfaces with desired emissivities, femtosecond laser surface processing (FLSP) has recently drawn enormous attention. Despite the versatility offered by FLSP, there is a knowledge gap in accurately predicting the outcome emissivity prior to fabrication. In this work, we demonstrate the immense advantage of employing artificial intelligence (AI) techniques to predict the emissivity of complex surfaces. For this aim, we used FLSP to fabricate 116 different aluminum samples. A comprehensive dataset was established by collecting surface characteristics, laser operating parameters, and the measured emissivities for all samples. We demonstrate the successful application of AI in two distinct scenarios: (1) effective emissivity classification solely based on 3D surface morphology images, and (2) emissivity prediction based on surface characteristics and FLSP parameters. These findings open new pathways towards extended implementation of AI to predict various surface properties in functionalized samples or extract the required fabrication parameters via reverse engineering

Detecting mechanical loosening of total hip replacement implant from plain radiograph using deep convolutional neural network

Plain radiography is widely used to detect mechanical loosening of total hip replacement (THR) implants. Currently, radiographs are assessed manually by medical professionals, which may be prone to poor inter and intra observer reliability and low accuracy. Furthermore, manual detection of mechanical loosening of THR implants requires experienced clinicians who might not always be readily available, potentially resulting in delayed diagnosis. In this study, we present a novel, fully automatic and interpretable approach to detect mechanical loosening of THR implants from plain radiographs using deep convolutional neural network (CNN). We trained a CNN on 40 patients anteroposterior hip x rays using five fold cross validation and compared its performance with a high volume board certified orthopaedic surgeon (AFC). To increase the confidence in the machine outcome, we also implemented saliency maps to visualize where the CNN looked at to make a diagnosis. CNN outperformed the orthopaedic surgeon in diagnosing mechanical loosening of THR implants achieving significantly higher sensitively (0.94) than the orthopaedic surgeon (0.53) with the same specificity (0.96). The saliency maps showed that the CNN looked at clinically relevant features to make a diagnosis. Such CNNs can be used for automatic radiologic assessment of mechanical loosening of THR implants to supplement the practitioners decision making process, increasing their diagnostic accuracy, and freeing them to engage in more patient centric care

Doctor of Philosophy

dissertationThe longevity of metal-on-polyethylene prosthetic hip implants, in which a CoCrMo femoral head articulates with a polyethylene acetabular liner, is often limited by polyethylene wear and osteolysis caused by polyethylene wear particles. Current approaches to reduce polyethylene wear include improving the mechanical properties of the polyethylene acetabular liner, and/or manufacturing ultra-smooth articulating surfaces. In contrast, we show that adding a patterned microtexture of concave "dimples" to a smooth CoCrMo surface significantly reduces polyethylene wear by promoting the formation of an elastohydrodynamic lubrication film, which reduces contact between the CoCrMo and polyethylene bearing surfaces. Using pin-on-disc (PoD) experiments in which a polyethylene pin articulates with a CoCrMo disc, we gravimetrically quantify polyethylene wear and demonstrate that a patterned microtexture on the CoCrMo disc significantly reduces polyethylene wear compared to a smooth, nontextured CoCrMo disc. We quantify wear of different polyethylene materials currently used in commercial prosthetic hip implants, articulating with several patterned microtexture geometries. We correlate polyethylene wear with surface topography measurements and conclude that the patterned microtexture creates microhydrodynamic bearings and an elastohydrodynamic lubricant film, which reduces contact between the articulating surfaces, thus reducing wear. We also perform electrochemical measurements to show that the microtexture does not negatively impact iv the corrosion resistance of CoCrMo. Furthermore, we use PoD experiments to measure friction between a polyethylene pin and microtextured CoCrMo discs, covering a wide range of operating conditions including sliding velocity and contact pressure. We determine how the lubrication regime changes as a function of operating conditions and show that the patterned microtexture accelerates the transition from boundary to elastohydrodynamic lubrication. Additionally, we illustrate that the patterned microtexture could enable tailoring the hip implant to specific patient needs based on activity level, gender, and age. Finally, we use machine learning methods to analyze published PoD polyethylene wear datasets and implement and cross-validate several model-based and instance-based data-driven models, and quantify their prediction error with respect to the published experiments. The data-driven models enable predicting polyethylene wear of PoD experiments based on its operating parameters, and they reveal the relative contribution of individual PoD operating parameters to the resulting polyethylene wear, thus potentially reducing the need for time consuming experiments

Application of deep learning for automated diagnosis and classification of hip dysplasia on plain radiographs

Abstract Background Hip dysplasia is a condition where the acetabulum is too shallow to support the femoral head and is commonly considered a risk factor for hip osteoarthritis. The objective of this study was to develop a deep learning model to diagnose hip dysplasia from plain radiographs and classify dysplastic hips based on their severity. Methods We collected pelvic radiographs of 571 patients from two single-center cohorts and one multicenter cohort. The radiographs were split in half to create hip radiographs (n = 1022). One orthopaedic surgeon and one resident assessed the radiographs for hip dysplasia on either side. We used the center edge (CE) angle as the primary diagnostic criteria. Hips with a CE angle  25° were labeled as dysplastic, borderline, and normal, respectively. The dysplastic hips were also classified with both Crowe and Hartofilakidis classification of dysplasia. The dataset was divided into train, validation, and test subsets using 80:10:10 split-ratio that were used to train two deep learning models to classify images into normal, borderline and (1) Crowe grade 1–4 or (2) Hartofilakidis grade 1–3. A pre-trained on Imagenet VGG16 convolutional neural network (CNN) was utilized by performing layer-wise fine-turning. Results Both models struggled with distinguishing between normal and borderline hips. However, achieved high accuracy (Model 1: 92.2% and Model 2: 83.3%) in distinguishing between normal/borderline vs. dysplastic hips. The overall accuracy of Model 1 was 68% and for Model 2 73.5%. Most misclassifications for the Crowe and Hartofilakidis classifications were +/- 1 class from the correct class. Conclusions This pilot study shows promising results that a deep learning model distinguish between normal and dysplastic hips with high accuracy. Future research and external validation are warranted regarding the ability of deep learning models to perform complex tasks such as identifying and classifying disorders using plain radiographs. Level of Evidence Diagnostic level I

The use of deep learning enables high diagnostic accuracy in detecting syndesmotic instability on weight-bearing CT scanning

Delayed diagnosis of syndesmosis instability can lead to significant morbidity and accelerated arthritic change in the ankle joint. Weight-bearing computed tomography (WBCT) has shown promising potential for early and reliable detection of isolated syndesmotic instability using 3D volumetric measurements. While these measurements have been reported to be highly accurate, they are also experience-dependent, time-consuming, and need a particular 3D measurement software tool that leads the clinicians to still show more interest in the conventional diagnostic methods for syndesmotic instability. The purpose of this study was to increase accuracy, accelerate analysis time, and reduce inter-observer bias by automating 3D volume assessment of syndesmosis anatomy using WBCT scans. We conducted a retrospective study using previously collected WBCT scans of patients with unilateral syndesmotic instability. 144 bilateral ankle WBCT scans were evaluated (48 unstable, 96 control). We developed three deep learning (DL) models for analyzing WBCT scans to recognize syndesmosis instability. These three models included two state-of-the-art models (Model 1 - 3D convolutional neural network [CNN], and Model 2 - CNN with long short-term memory [LSTM]), and a new model (Model 3 - differential CNN LSTM) that we introduced in this study. Model 1 failed to analyze the WBCT scans (F1-score = 0). Model 2 only misclassified two cases (F1-score = 0.80). Model 3 outperformed Model 2 and achieved a nearly perfect performance, misclassifying only one case (F1-score = 0.91) in the control group as unstable while being faster than Model 2