BACKGROUND: Robot-assisted gait training is incorporated into guidelines for stroke rehabilitation. It is a promising tool combined with conventional therapy for low ambulatory patients. The heavy weight and bulky appearance of a robotic exoskeleton limits its practicality. On the other hand, soft robotic exosuit (SRE) based on its light weight and inconspicuous property, is better tolerated by patients in daily life. The aim of this study is to review the efficacy of the SRE with regard to walking ability and biomechanical properties in stroke patients. METHODS: Electronic searches were carried out in PubMed, Embase, Cochrane Library, Web of Science, and the Physiotherapy Evidence Database. Clinical trials that investigated the effectiveness of SREs on ambulation ability in patients with post-stroke hemiparesis were eligible. Qualitative data synthesis was subsequently performed. RESULTS: Nine studies were identified as relevant, involving a total of 83 patients. For the assessment of SRE efficacy, outcome measures were walking ability and biomechanical properties. In terms of both immediate effect and training effect, SREs improved the walking speed, walking distance, peak ankle dorsiflexion angle during swing phase, peak paretic propulsion, stride length and compensated gait in stroke patients. CONCLUSIONS: SRE improved the ambulation ability of stroke patients in terms of walking ability and biomechanical properties. The small number of studies limits the generalizability of interpretation. More controlled studies with better quality are required to reach a more solid conclusion on this issue.

BACKGROUND: Running with the appropriate intensity may produce a positive influence on the mechanical properties of cortical bone structure. However, few studies have discussed the effects of different running intensities on the mechanical properties at different levels, especially at the micro-level, because the micromechanical parameters are difficult to measure experimentally. METHODS: An approach that combines finite element analysis and experimental data was proposed to predict a micromechanical parameter in the rat femoral cortical bone structure, namely, the micro-level failure strain. Based on the previous three-point bending experimental information, fracture simulations were performed on the femur finite element models to predict their failure process under the same bending load, and the micro-level failure strains in tension and compression of these models were back-calculated by fitting the experimental load-displacement curves. Then, the effects of different running intensities on the micro-level failure strain of rat femoral cortical bone structure were investigated. RESULTS: The micro-level failure strains of the cortical bone structures expressed statistical variations under different running intensities, which indicated that different mechanical stimuli of running had significant influences on the micromechanical properties. The greatest failure strain occurred in the cortical bone structure under low-intensity running, and the lowest failure strain occurred in the structure under high-intensity running. CONCLUSIONS: Moderate and low-intensity running were effective in enhancing the micromechanical properties, whereas high-intensity running led to the weakening of the micromechanical properties of cortical bone. Based on these, the changing trends in the micromechanical properties were exhibited, and the effects of different running intensities on the fracture performance of rat cortical bone structures could be discussed in combination with the known mechanical parameters at the macro- and nano-levels, which provided the theoretical basis for reducing fracture incidence through running exercise.

Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.

BACKGROUND: Motor imagery is a cognitive process of imagining a performance of a motor task without employing the actual movement of muscles. It is often used in rehabilitation and utilized in assistive technologies to control a brain-computer interface (BCI). This paper provides a comparison of different time-frequency representations (TFR) and their Rényi and Shannon entropies for sensorimotor rhythm (SMR) based motor imagery control signals in electroencephalographic (EEG) data. The motor imagery task was guided by visual guidance, visual and vibrotactile (somatosensory) guidance or visual cue only. RESULTS: When using TFR-based entropy features as an input for classification of different interaction intentions, higher accuracies were achieved (up to 99.87%) in comparison to regular time-series amplitude features (for which accuracy was up to 85.91%), which is an increase when compared to existing methods. In particular, the highest accuracy was achieved for the classification of the motor imagery versus the baseline (rest state) when using Shannon entropy with Reassigned Pseudo Wigner-Ville time-frequency representation. CONCLUSIONS: Our findings suggest that the quantity of useful classifiable motor imagery information (entropy output) changes during the period of motor imagery in comparison to baseline period; as a result, there is an increase in the accuracy and F1 score of classification when using entropy features in comparison to the accuracy and the F1 of classification when using amplitude features, hence, it is manifested as an improvement of the ability to detect motor imagery.

BACKGROUND: Unilateral diaphragmatic paralysis in patients with univentricular heart is a known complication after pediatric cardiac surgery. Because diaphragmatic excursion has a significant influence on perfusion of the pulmonary arteries and hemodynamics in these patients, unilateral loss of function leads to multiple complications. The current treatment of choice, diaphragmatic plication, does not lead to a full return of function. A unilateral diaphragmatic pacemaker has shown potential as a new treatment option. In this study, we investigated an accelerometer as a trigger for a unilateral diaphragm pacemaker (closed-loop system). METHODS: Seven pigs (mean weight 20.7 ± 2.25 kg) each were implanted with a customized accelerometer on the right diaphragmatic dome. Accelerometer recordings (mV) of the diaphragmatic excursion of the right diaphragm were compared with findings using established methods (fluoroscopy [mm]; ultrasound, M-mode [cm]). For detection of the amplitude of diaphragmatic excursions, the diaphragm was stimulated with increasing amperage by a cuff electrode implanted around the right phrenic nerve. RESULTS: Results with the different techniques for measuring diaphragmatic excursions showed correlations between accelerometer and fluoroscopy values (correlation coefficient 0.800, P < 0.001), accelerometer and ultrasound values (0.883, P < 0.001), and fluoroscopy and ultrasound values (0.816, P < 0.001). CONCLUSION: The accelerometer is a valid method for detecting diaphragmatic excursion and can be used as a trigger for a unilateral diaphragmatic pacemaker.

OBJECTIVES: Use of an AI system based on deep learning to investigate whether the system can aid in distinguishing malignant from benign calcifications on spot magnification mammograms, thus potentially reducing unnecessary biopsies. METHODS: In this retrospective study, we included public and in-house datasets with annotations for the calcifications on both craniocaudal and mediolateral oblique vies, or both craniocaudal and mediolateral views of each case of mammograms. All the lesions had pathological results for correlation. Our system comprised an algorithm based on You Only Look Once (YOLO) named adaptive multiscale decision fusion module. The algorithm was pre-trained on a public dataset, Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), then re-trained and tested on the in-house dataset of spot magnification mammograms. The performance of the system was investigated by receiver operating characteristic (ROC) analysis. RESULTS: We included 1872 images from 753 calcification cases (414 benign and 339 malignant) from CBIS-DDSM. From the in-house dataset, 636 cases (432 benign and 204 malignant) with 1269 spot magnification mammograms were included, with all lesions being recommended for biopsy by radiologists. The area under the ROC curve for our system on the in-house testing dataset was 0.888 (95% CI 0.868-0.908), with a sensitivity of 88.4% (95% CI 86.9-8.99%), specificity of 80.8% (95% CI 77.6-84%), and an accuracy of 84.6% (95% CI 81.8-87.4%) at the optimal cutoff value. Using the system with two views of spot magnification mammograms, 80.8% benign biopsies could be avoided. CONCLUSION: The AI system showed good accuracy for classification of calcifications on spot magnification mammograms which were all categorized as suspicious by radiologists, thereby potentially reducing unnecessary biopsies.

Open-globe injury is a common cause of blindness clinically caused by blunt trauma, sharp injury, or shock waves, characterised by rupture of the cornea or sclera and exposure of eye contents to the environment. It causes catastrophic damage to the globe, resulting in severe visual impairment and psychological trauma to the patient. Depending on the structure of the globe, the biomechanics causing ocular rupture can vary, and trauma to different parts of the globe can cause varying degrees of eye injury. The weak parts or parts of the eyeball in contact with foreign bodies rupture when biomechanics, such as external force, unit area impact energy, corneoscleral stress, and intraocular pressure exceed a certain value. Studying the biomechanics of open-globe injury and its influencing factors can provide a reference for eye-contact operations and the design of eye-protection devices. This review summarises the biomechanics of open-globe injury and the relevant factors.

Tracking points in ultrasound (US) videos can be especially useful to characterize tissues in motion. Tracking algorithms that analyze successive video frames, such as variations of Optical Flow and Lucas-Kanade (LK), exploit frame-to-frame temporal information to track regions of interest. In contrast, convolutional neural-network (CNN) models process each video frame independently of neighboring frames. In this paper, we show that frame-to-frame trackers accumulate error over time. We propose three interpolation-like methods to combat error accumulation and show that all three methods reduce tracking errors in frame-to-frame trackers. On the neural-network end, we show that a CNN-based tracker, DeepLabCut (DLC), outperforms all four frame-to-frame trackers when tracking tissues in motion. DLC is more accurate than the frame-to-frame trackers and less sensitive to variations in types of tissue movement. The only caveat found with DLC comes from its non-temporal tracking strategy, leading to jitter between consecutive frames. Overall, when tracking points in videos of moving tissue, we recommend using DLC when prioritizing accuracy and robustness across movements in videos, and using LK with the proposed error-correction methods for small movements when tracking jitter is unacceptable.

BACKGROUND: Over the decades, many publications have established respiratory muscle training (RMT) as an effective way in improving respiratory dysfunction in multiple populations. The aim of the paper is to determine the trend of research and multidisciplinary collaboration in publications related to RMT over the last 6 decades. The authors also sought to chart the advancement of RMT among people with spinal cord injury (SCI) over the last 60 years. METHODS: Bibliometric analysis was made, including the publications' profiles, citation analysis and research trends of the relevant literature over the last 60 years. Publications from all time frames were retrieved from Scopus database. A subgroup analysis of publications pertinent to people with SCI was also made. RESULTS: Research on RMT has been steadily increasing over the last 6 decades and across geographical locations. While medicine continues to dominate the research on RMT, this topic also continues to attract researchers and publications from other areas such as engineering, computer science and social science over the last 10 years. Research collaboration between authors in different backgrounds was observed since 2006. Source titles from non-medical backgrounds have also published articles pertinent to RMT. Among people with SCI, researchers utilised a wide range of technology from simple spirometers to electromyography in both intervention and outcome measures. With various types of interventions implemented, RMT generally improves pulmonary function and respiratory muscle strength among people with SCI. CONCLUSIONS: While research on RMT has been steadily increasing over the last 6 decades, more collaborations are encouraged in the future to produce more impactful and beneficial research on people who suffer from respiratory disorders.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide, and is related to disturbed lipid metabolism and redox homeostasis. However, a definitive drug treatment has not been approved for this disease. Studies have found that electromagnetic fields (EMF) can ameliorate hepatic steatosis and oxidative stress. Nevertheless, the mechanism remains unclear. METHODS: NAFLD models were established by feeding mice a high-fat diet. Simultaneously, EMF exposure is performed. The effects of the EMF on hepatic lipid deposition and oxidative stress were investigated. Additionally, the AMPK and Nrf2 pathways were analysed to confirm whether they were activated by the EMF. RESULTS: Exposure to EMF decreased the body weight, liver weight and serum triglyceride (TG) levels and restrained the excessive hepatic lipid accumulation caused by feeding the HFD. The EMF boosted CaMKKβ protein expression, activated AMPK phosphorylation and suppressed mature SREBP-1c protein expression. Meanwhile, the activity of GSH-Px was enhanced following an increase in nuclear Nrf2 protein expression by PEMF. However, no change was observed in the activities of SOD and CAT. Consequently, EMF reduced hepatic reactive oxygen species (ROS) and MDA levels, which means that EMF relieved liver damage caused by oxidative stress in HFD-fed mice. CONCLUSIONS: EMF may activate the CaMKKβ/AMPK/SREBP-1c and Nrf2 pathways to control hepatic lipid deposition and oxidative stress. This investigation indicates that EMF may be a novel therapeutic method for NAFLD.

PURPOSE: To provide a summary of the research advances on ocular images-based artificial intelligence on systemic diseases. METHODS: Narrative literature review. RESULTS: Ocular images-based artificial intelligence has been used in a variety of systemic diseases, including endocrine, cardiovascular, neurological, renal, autoimmune, and hematological diseases, and many others. However, the studies are still at an early stage. The majority of studies have used AI only for diseases diagnosis, and the specific mechanisms linking systemic diseases to ocular images are still unclear. In addition, there are many limitations to the research, such as the number of images, the interpretability of artificial intelligence, rare diseases, and ethical and legal issues. CONCLUSION: While ocular images-based artificial intelligence is widely used, the relationship between the eye and the whole body should be more clearly elucidated.

Recent advances in deep learning have shown great potential for the automatic generation of medical imaging reports. Deep learning techniques, inspired by image captioning, have made significant progress in the field of diagnostic report generation. This paper provides a comprehensive overview of recent research efforts in deep learning-based medical imaging report generation and proposes future directions in this field. First, we summarize and analyze the data set, architecture, application, and evaluation of deep learning-based medical imaging report generation. Specially, we survey the deep learning architectures used in diagnostic report generation, including hierarchical RNN-based frameworks, attention-based frameworks, and reinforcement learning-based frameworks. In addition, we identify potential challenges and suggest future research directions to support clinical applications and decision-making using medical imaging report generation systems.

BACKGROUND: Mechanical ventilation is an essential component in the treatment of patients with acute respiratory distress syndrome. Prompt adaptation of the settings of a ventilator to the variable needs of patients is essential to ensure personalised and protective ventilation. Still, it is challenging and time-consuming for the therapist at the bedside. In addition, general implementation barriers hinder the timely incorporation of new evidence from clinical studies into routine clinical practice. RESULTS: We present a system combing clinical evidence and expert knowledge within a physiological closed-loop control structure for mechanical ventilation. The system includes multiple controllers to support adequate gas exchange while adhering to multiple evidence-based components of lung protective ventilation. We performed a pilot study on three animals with an induced ARDS. The system achieved a time-in-target of over 75 % for all targets and avoided any critical phases of low oxygen saturation, despite provoked disturbances such as disconnections from the ventilator and positional changes of the subject. CONCLUSIONS: The presented system can provide personalised and lung-protective ventilation and reduce clinician workload in clinical practice.

PURPOSE: This study aimed to develop an interpretable machine learning model to predict the onset of myopia based on individual daily information. METHOD: This study was a prospective cohort study. At baseline, non-myopia children aged 6-13 years old were recruited, and individual data were collected through interviewing students and parents. One year after baseline, the incidence of myopia was evaluated based on visual acuity test and cycloplegic refraction measurement. Five algorithms, Random Forest, Support Vector Machines, Gradient Boosting Decision Tree, CatBoost and Logistic Regression were utilized to develop different models and their performance was validated by area under curve (AUC). Shapley Additive exPlanations was applied to interpret the model output on the individual and global level. RESULT: Of 2221 children, 260 (11.7%) developed myopia in 1 year. In univariable analysis, 26 features were associated with the myopia incidence. Catboost algorithm had the highest AUC of 0.951 in the model validation. The top 3 features for predicting myopia were parental myopia, grade and frequency of eye fatigue. A compact model using only 10 features was validated with an AUC of 0.891. CONCLUSION: The daily information contributed reliable predictors for childhood's myopia onset. The interpretable Catboost model presented the best prediction performance. Oversampling technology greatly improved model performance. This model could be a tool in myopia preventing and intervention that can help identify children who are at risk of myopia, and provide personalized prevention strategies based on contributions of risk factors to the individual prediction result.

OBJECTIVES: Optical Coherence Tomograph (OCT) imaging technology can be used to examine, in vivo, the human ET. At present, it is impossible to achieve the OCT scanning vivo and ex vivo in the same individual human body, or study the consistency between OCT images and histological images of the eustachian tube nasopharyngeal region and adjacent structures. The aim of this study was to determine the consistency between OCT images and histological sections in vivo and ex vivo in miniature pigs. METHODS: OCT imaging was performed on five adult miniature pigs in vivo and ex vivo. The images of the eustachian tube OCT (ET-OCT), nasopharynx OCT (NP-OCT) and histological cross sections were further studied. RESULTS: All five miniature pigs achieved the OCT scan successfully, acquiring ET-OCT and NP-OCT images in vivo and ex vivo on both sides. The acquired ET OCT images closely matched the histological images, revealing details of the cartilage, submucosa, glands, and mucosa. The lower segment of the ET wall mucosa had an abundance of glands and submucosal tissues, with more low-signal areas appearing in the ex vivo images. The NP-OCT images of the nasopharynx matched the details of the mucosa and submucosal tissues. The ex-vivo OCT images showed thicker mucosa and more scattered slightly lower signal areas compared to the vivo OCT images. CONCLUSIONS: ET-OCT images and NP-OCT images matched the histological structure of eustachian tube nasopharyngeal region structures in miniature pigs both in vivo and ex vivo. OCT images may be sensitive to changes in edema and ischemia status. There is a great potential for morphological assessment of inflammation, edema, injure, mucus gland status.

BACKGROUND: Since myocardial work (MW) and left atrial strain are valuable for screening coronary artery disease (CAD), this study aimed to develop a novel CAD screening approach based on machine learning-enhanced echocardiography. METHODS: This prospective study used data from patients undergoing coronary angiography, in which the novel echocardiography features were extracted by a machine learning algorithm. A total of 818 patients were enrolled and randomly divided into training (80%) and testing (20%) groups. An additional 115 patients were also enrolled in the validation group. RESULTS: The superior diagnosis model of CAD was optimized using 59 echocardiographic features in a gradient-boosting classifier. This model showed that the value of the receiver operating characteristic area under the curve (AUC) was 0.852 in the test group and 0.834 in the validation group, with high sensitivity (0.952) and low specificity (0.691), suggesting that this model is very sensitive for detecting CAD, but its low specificity may increase the high false-positive rate. We also determined that the false-positive cases were more susceptible to suffering cardiac events than the true-negative cases. CONCLUSIONS: Machine learning-enhanced echocardiography can improve CAD detection based on the MW and left atrial strain features. Our developed model is valuable for estimating the pre-test probability of CAD and screening CAD patients in clinical practice. TRIAL REGISTRATION: Registered as NCT03905200 at ClinicalTrials.gov. Registered on 5 April 2019.

BACKGROUND: Although minimally invasive surgeries have gained popularity in many orthopaedic fields, minimally invasive approaches for diaphyseal clavicular fracture have not been widely performed, which is attributed to difficulties in performing a closed reduction of fracture deformities of a curved bone in a three-dimensional space. The goal of this study was to investigate the radiographic parameters of fracture deformities in a three-dimensional space and to identify the risk factors for deformities. METHODS: The computed tomography images of 100 patients who sustained a clavicle fracture were included. Five parameters were used to analyze the deformities: change in clavicle length, fracture displacement, and fragment rotation around the X, Y, Z axes. The change in length was assessed using the length of the endpoint line. The displacement was assessed using the distance between the fracture midpoints. The rotation deformities were assessed using the Euler angles. The correlation between the parameters was evaluated with the Pearson correlation coefficient. The risk factors were evaluated using univariable analysis and multiple regression analysis. RESULTS: The average change in length was - 5.3 ± 8.3 mm. The displacement was 11.8 ± 7.1 mm. The Euler angles in the Z-Y-X sequences were -1 ± 8, 1 ± 8, and - 8 ± 13 degrees. The correlation coefficient between the change in length and the displacement was - 0.724 (p < 0.001). The variables found to increase the risk of shortening and displacement were right-sided fracture (p = 0.037), male sex (p = 0.015), and multifragmentary type (p = 0.020). The variables found to increase the risk of rotation deformity were the number of rib fractures (p = 0.001) and scapula fracture (p = 0.025). CONCLUSIONS: There was a strong correlation between shortening and displacement. The magnitude of anterorotation around the X axis was greater than the magnitude of retraction around the Z axis and depression around the Y axis. The risk factors for shortening and displacement included right-sided fracture, male sex, and multifragmentary type. The risk factor for retraction around the Z axis was the number of rib fractures, and the risk factor for depression around the Y axis was scapula fracture. These results could be useful adjuncts in guiding minimally invasive surgical planning for diaphyseal clavicular fractures.

BACKGROUND: Adaptive gait involves the ability to adjust the leading foot in response to the requirement of dynamic environments during walking. Accurate adjustments of the minimum toe clearance (MTC) height and step length can prevent older people from falling when walking and responding to hazards. Although older people with diabetes fall more frequently than healthy older adults, no previous studies have quantified their adaptive gait abilities. This study aimed to investigate the effects of diabetes mellitus on step length and MTC height adjustments using a non-immersive virtual-reality system. METHODS: Sixteen young adults (26 ± 5 years, 7 females), 16 healthy older adults (68 ± 5 years, 6 females), and 16 older adults with diabetes (70 ± 5 years, 6 females) completed adaptability tests while walking on a treadmill. A computer system visualised a continuous real-time signal of absolute step length and MTC on a monitor. Each person responded to four discrete participant-specific step length and MTC visual targets that were presented on the same signal. Tasks were to match the peaks of interest on each signal to presented targets. Targets were 10% longer or shorter than the mean baseline step length, and 2.5 cm, and 3.5 cm higher than the mean baseline MTC. When a target was displayed, it remained unchanged for 10 consecutive foot displacement adaptation attempts. Then, the target was removed and a new target or the same target was present after 10 consecutive steps and remained for 10 steps. Each target was randomly presented three times (3 × 10). Step length and MTC height adjustments in response to targets were measured and compared among groups. RESULTS: Mean preferred walking speeds were not different among groups significantly when no targets were presented on the monitor in baseline walking. However, when participants walked on a treadmill while attempting to match step lengths or MTC heights to displayed targets on the monitor, the group with diabetes had reduced step length and MTC adjustments compared with other groups significantly. They showed greater errors (differences between their step lengths/MTC heights and presented targets) on the monitor. CONCLUSIONS: This study quantified reduced abilities for older individuals with diabetes to adjust both step length and MTC in response to stimuli compared to healthy older counterparts. Reduced step length and MTC height adjustments can increase falls in at risk populations. The presented virtual-reality system has merits for assessing and training step and MTC adaptation.

Signal analysis is a domain which is an amalgamation of different processes coming together to form robust pipelines for the automation of data analysis. When applied to the medical world, physiological signals are used. It is becoming increasingly common in today's day and age to be working with very large datasets, on the scale of having thousands of features. This is largely due to the fact that the acquisition of biomedical signals can be taken over multi-hour timeframes, which is another challenge to solve in and of itself. This paper will focus on the electrocardiogram (ECG) signal specifically, and common feature extraction techniques used for digital health and artificial intelligence (AI) applications. Feature extraction is a vital step of biomedical signal analysis. The basic goal of feature extraction is for signal dimensionality reduction and data compaction. In simple terms, this would allow one to represent data with a smaller subset of features; these features could then later be leveraged to be used more efficiently for machine learning and deep learning models for applications, such as classification, detection, and automated applications. In addition, the redundant data in the overall dataset is filtered out as the data is reduced during feature extraction. In this review, we cover ECG signal processing and feature extraction in the time domain, frequency domain, time-frequency domain, decomposition, and sparse domain. We also provide pseudocode for the methods discussed so that they can be replicated by practitioners and researchers in their specific areas of biomedical work. Furthermore, we discuss deep features, and machine learning integration, to complete the overall pipeline design for signal analysis. Finally, we discuss future work that can be innovated upon in the feature extraction domain for ECG signal analysis.

BACKGROUND: Isolated ACL reconstructions (ACLR) demonstrate limitations in restoring native knee kinematics. This study investigates the knee mechanics of ACLR plus various anterolateral augmentations using a patient-specific musculoskeletal knee model. MATERIALS AND METHODS: A patient-specific knee model was developed in OpenSim using contact surfaces and ligament details derived from MRI and CT data. The contact geometry and ligament parameters were varied until the predicted knee angles for intact and ACL-sectioned models were validated against cadaveric test data for that same specimen. Musculoskeletal models of the ACLR combined with various anterolateral augmentations were then simulated. Knee angles were compared between these reconstruction models to determine which technique best matched the intact kinematics. Also, ligament strains calculated by the validated knee model were compared to those of the OpenSim model driven by experimental data. The accuracy of the results was assessed by calculating the normalised RMS error (NRMSE); an NRMSE < 30% was considered acceptable. RESULTS: All rotations and translations predicted by the knee model were acceptable when compared to the cadaveric data (NRMSE < 30%), except for the anterior/posterior translation (NRMSE > 60%). Similar errors were observed between ACL strain results (NRMSE > 60%). Other ligament comparisons were acceptable. All ACLR plus anterolateral augmentation models restored kinematics toward the intact state, with ACLR plus anterolateral ligament reconstruction (ACLR + ALLR) achieving the best match and the greatest strain reduction in ACL, PCL, MCL, and DMCL. CONCLUSION: The intact and ACL-sectioned models were validated against cadaveric experimental results for all rotations. It is acknowledged that the validation criteria are very lenient; further refinement is required for improved validation. The results indicate that anterolateral augmentation moves the kinematics closer to the intact knee state; combined ACLR and ALLR provide the best outcome for this specimen.