Scientists from ExplantLab have identified a genotype that is associated with joint replacement failure in some patients. Based on these findings, the scientists developed a machine-learning algorithm called Orthotype, which uses a patient's genotype and other factors to accurately predict the outcome of joint replacement surgery. More than five million joint replacements are performed globally each year. Although most patients are satisfied with the results of their surgery, a significant number of joint replacements fail early, following adverse immune responses. One of the most popular implant materials used in joint replacements is cobalt chrome (CoCr).
With the growth of artificial intelligence and machine learning in healthcare, even prosthetic limbs are becoming smart. These smart prosthetics can combine manual control with machine learning for more accessible and effective use. We are seeing a growth of machine learning in healthcare, where it is used to improve a patient's overall health, including providing accurate diagnosis and better treatment plans. Additionally, machine learning (ML) can also understand healthcare data by improving diagnostics and predicting accurate outcomes. One of the latest fields where AI and ML have been making an impact is prosthetics.
Fragility hip fracture increases morbidity and mortality in older adult patients, especially within the first year. Identification of patients at high risk of death facilitates modification of associated perioperative factors that can reduce mortality. Various machine learning algorithms have been developed and are widely used in healthcare research, particularly for mortality prediction. This study aimed to develop and internally validate 7 machine learning models to predict 1-year mortality after fragility hip fracture. This retrospective study included patients with fragility hip fractures from a single center (Siriraj Hospital, Bangkok, Thailand) from July 2016 to October 2018. A total of 492 patients were enrolled. They were randomly categorized into a training group (344 cases, 70%) or a testing group (148 cases, 30%). Various machine learning techniques were used: the Gradient Boosting Classifier (GB), Random Forests Classifier (RF), Artificial Neural Network Classifier (ANN), Logistic Regression Classifier (LR), Naive Bayes Classifier (NB), Support Vector Machine Classifier (SVM), and K-Nearest Neighbors Classifier (KNN). All models were internally validated by evaluating their performance and the area under a receiver operating characteristic curve (AUC). For the testing dataset, the accuracies were GB model = 0.93, RF model = 0.95, ANN model = 0.94, LR model = 0.91, NB model = 0.89, SVM model = 0.90, and KNN model = 0.90. All models achieved high AUCs that ranged between 0.81 and 0.99. The RF model also provided a negative predictive value of 0.96, a positive predictive value of 0.93, a specificity of 0.99, and a sensitivity of 0.68. Our machine learning approach facilitated the successful development of an accurate model to predict 1-year mortality after fragility hip fracture. Several machine learning algorithms (eg, Gradient Boosting and Random Forest) had the potential to provide high predictive performance based on the clinical parameters of each patient. The web application is available at www.hipprediction.com . External validation in a larger group of patients or in different hospital settings is warranted to evaluate the clinical utility of this tool. Thai Clinical Trials Registry (22 February 2021; reg. no. TCTR20210222003 ).
Prosthetic limbs have been slow to evolve from simple motionless replicas of human body parts to moving, active devices. A major part of this is that controlling the many joints of a prosthetic is no easy task. However, researchers have worked to simplify this task, by capturing nerve signals and allowing deep learning routines to figure the rest out. Reported in a pre-published paper, researchers used implanted electrodes to capture signals from the median and ulnar nerves in the forearm of Shawn Findley, who had lost a hand to a machine shop accident 17 years prior. An AI decoder was then trained to decipher signals from the electrodes using an NVIDIA Titan X GPU.
Qiuchong Chen,1,* Yixue Zhang,1,* Mengjun Zhang,1 Ziying Li,1 Jindong Liu1,2 1Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, People's Republic of China; 2Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical University, Xuzhou, Jiangsu, People's Republic of China *These authors contributed equally to this work Correspondence: Jindong Liu, Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, 99 Huaihai Road West, Quanshan District, Xuzhou, Jiangsu, 221000, People's Republic of China, Email [email protected] Objective: There has been a worldwide increment in acute kidney injury (AKI) incidence among elderly orthopedic operative patients. The AKI prediction model provides patients' early detection a possibility at risk of AKI; most of the AKI prediction models derive, however, from the cardiothoracic operation. The purpose of this study is to predict the risk of AKI in elderly patients after orthopedic surgery based on machine learning algorithm models. Methods: We organized a retrospective study being comprised of 1000 patients with postoperative AKI undergoing orthopedic surgery from September 2016, to June, 2021. They were divided into training (80%;n 799) and test (20%;n 201) sets.We utilized nine machine learning (ML) algorithms and used intraoperative information and preoperative clinical features to acquire models to predict AKI. The performance of the model was evaluated according to the area under the receiver operating characteristic (AUC), sensitivity, specificity and accuracy. Select the optimal model and establish the nomogram to make the prediction model visualization. The concordance statistic (C-statistic) and calibration curve were used to discriminate and calibrate the nomogram respectively. Results: In predicting AKI, nine ML algorithms posted AUC of 0.656– 1.000 in the training cohort, with the randomforest standing out and AUC of 0.674– 0.821 in the test cohort, with the logistic regression model standing out.
Johnson & Johnson has signed up Australian software engineering company Max Kelsen to use artificial intelligence to speed up the supply of surgical instruments to hospitals, helping to alleviate the backlog of surgeries that built up during the COVID-19 pandemic. The US pharmaceutical and medical device behemoth said it had begun to use Max Kelsen's SAVI platform, which uses cameras and machine learning to automatically identify and catalogue surgical instruments after they are used in an operation. A single operation can take 10 trays of complex surgical instruments, says Warwick Shaw, customer solutions partner, Johnson & Johnson Medical. The system, which runs part on an iPad and part on machine learning models in the cloud, cuts by at least 40 per cent the time it takes to catalogue the instruments, ensure they are contamination free and in working order, and pass them on to the next hospital, Johnson & Johnson said. While hospitals generally own their own surgical instruments for departments such as accident and emergency, the instruments for specialist surgeries such as knee replacements and spinal fusions are typically checked in and out from equipment rental companies such as Johnson & Johnson like books from a library.
Machine-learning methods and neural networks offer a new and powerful approach to automate diagnostics and outcome prediction, so this new technique we've shared has great potential. Despite fracture classification so strongly determining surgical treatment and hence patient outcomes, there is currently no standardized process as to who determines this classification in the UK – whether this is done by orthopedic surgeons or radiologists specializing in musculoskeletal disorders,
A 3D-printed prosthetic hand controlled using a new AI-based approach could significantly lower the cost of bionic limbs for amputees. Real need: There are approximately 540,000 upper-limb amputees in the United States, but sophisticated "myoelectric" prosthetics, controlled by muscle contractions, are still very expensive. Such devices cost between $25,000 and $75,000 (not including maintenance and repair), and they can be difficult to use because it is hard for software to distinguish between different muscle flexes. Handy invention: Researchers in Japan came up with a cheaper, smarter myoelectric device. Their five-fingered, 3D-printed hand is controlled using a neural network trained to recognize combined signals--or, as they call them, "muscle synergies."
Below, a prosthetic hand robot used by the team. Researchers at Shenyang University of Technology and the University of Electro-Communications in Tokyo are trying to figure out how to make prosthetic hands respond to arm movements. For the last decade, scientists have been trying to figure out how to use surface electromyography (EMG) signals to control prosthetic limbs. EMG signals are electrical signals that cause our muscles to contract. They can be recorded by inserting electrode needles into the muscle.
Artificial intelligence (AI) in healthcare is rapidly growing and offers novel options of data analysis. Machine learning (ML) represents a distinct application of AI, which is capable of generating predictions and has already been tested in different medical specialties with various approaches such as diagnostic applications, cost predictions or identification of risk factors. In orthopaedics, this technology has only recently been introduced and the literature on ML in knee arthroplasty is scarce. In this review, we aim to investigate which predictions are already feasible using ML models in knee arthroplasty to identify prerequisites for the effective use of this novel approach. For this reason, we conducted a systematic review of ML algorithms for outcome prediction in knee arthroplasty.