A markerless pipeline to analyze spontaneous movements of preterm infants

Comparison of marker-less 2D image-based methods for infant pose estimation

In this study we compare the performance of available generic- and specialized infant-pose estimators for a video-based automated general movement assessment (GMA), and the choice of viewing angle for optimal recordings, i.e., conventional diagonal view used in GMA vs. top-down view. We used 4500 annotated video-frames from 75 recordings of infant spontaneous motor functions from 4 to 16 weeks. To determine which pose estimation method and camera angle yield the best pose estimation accuracy on infants in a GMA related setting, the error with respect to human annotations and the percentage of correct key-points (PCK) were computed and compared. The results show that the best performing generic model trained on adults, ViTPose, also performs best on infants. We see no improvement from using specific infant-pose estimators over the generic pose estimators on our infant dataset. However, when retraining a generic model on our data, there is a significant improvement in pose estimation accuracy. This indicates limited generalization capabilities of infant-pose estimators to other infant datasets, meaning that one should be careful when choosing infant pose estimators and using them on infant datasets which they were not trained on. The pose estimation accuracy obtained from the top-down view is significantly better than that obtained from the diagonal view (the standard view for GMA). This suggests that a top-down view should be included in recording setups for automated GMA research.

Gradual restoration of gait following unicompartmental knee arthroplasty: a prospective study

BACKGROUND

This study investigates the gait characteristics and clinical outcomes following Unicompartmental Knee Arthroplasty (UKA) to provide scientific evidence for optimizing postoperative rehabilitation and patient management.

METHODS

Between December 2022 and November 2023, 34 patients with unilateral medial compartment knee osteoarthritis (KOA) underwent UKA. Preoperative and postoperative videos of patients in standing, walking (side view), squatting, and supine knee-bending positions were captured using smartphones. Gait parameters including gait cycle, swing time, swing phase, stance time, stance phase, double support time, walking speed, step time, cadence, step length, stride length, stride width, active knee flexion angle, and maximum hip and knee flexion angles during squatting were analyzed using the MediaPipe framework for human pose estimation.

RESULTS

Postoperative WOMAC scores were significantly lower than preoperative scores (P < 0.001), while postoperative KSS scores were significantly higher than preoperative scores (P < 0.001).Compared to preoperatively, postoperative affected-side gait speed, step length, step width, and active knee flexion angle all increased (P < 0.05). Additionally, postoperative gait cycle time and double-limb support time were reduced compared to preoperative values (P < 0.05). Among the 17 patients who could perform squats preoperatively and postoperatively, the maximum knee flexion angle and hip flexion angle in the squat position increased from preoperative values of (96.41 ± 20.65)° and (113.77 ± 22.56)° to postoperative values of (110.15 ± 20.79)° and (124.84 ± 21.13)°.

CONCLUSIONS

UKA significantly enhances knee joint kinematics, facilitating the transition from basic to advanced functional activities.

A systematic review of portable technologies for the early assessment of motor development in infants

Early screening and evaluation of infant motor development are crucial for detecting motor deficits and enabling timely interventions. Traditional clinical assessments are often subjective, without fully capturing infants' "real-world" behavior. This has sparked interest in portable, low-cost technologies to objectively and precisely measure infant motion at home, with a goal of enhancing ecological validity. In this systematic review, we explored the current landscape of portable, technology-based solutions to assess early motor development (within the first year), outlining the prevailing challenges and future directions. We reviewed 66 publications, which utilized video, sensors, or a combination of technologies. There were three key applications of these technologies: (1) automating clinical assessments, (2) illuminating new measures of motor development, and (3) predicting developmental outcomes. There was a promising trend toward earlier and more accurate detection using portable technologies. Additional research and demographic diversity are needed to develop fully automated, robust, and user-friendly tools. Registration & Protocol OSF Registries https://doi.org/10.17605/OSF.IO/R6JAE .

Marker-Less Video Analysis of Infant Movements for Early Identification of Neurodevelopmental Disorders

Background/Objectives: The early identification of neurodevelopmental disorders (NDDs) in infants is crucial for effective intervention and improved long-term outcomes. Recent evidence indicates a correlation between deficits in spontaneous movements in newborns and the likelihood of developing NDDs later in life. This study aims to address this aspect by employing a marker-less Artificial Intelligence (AI) approach for the automatic assessment of infants' movements from single-camera video recordings. Methods: A total of 74 high-risk infants were selected from the Italian Network for Early Detection of Autism Spectrum Disorders (NIDA) database and closely observed at five different time points, ranging from 10 days to 24 weeks of age. Automatic motion tracking was performed using deep learning to capture infants' body landmarks and extract a set of kinematic parameters. Results: Our findings revealed significant differences between infants later diagnosed with NDD and typically developing (TD) infants in three lower limb features at 10 days old: 'Median Velocity', 'Area differing from moving average', and 'Periodicity'. Using a Support Vector Machine (SVM), we achieved an accuracy rate of approximately 85%, a sensitivity of 64%, and a specificity of 100%. We also observed that the disparities in lower limb movements diminished over time points. Furthermore, the tracking accuracy was assessed through a comparative analysis with a validated semi-automatic algorithm (Movidea), obtaining a Pearson correlation (R) of 93.96% (88.61-96.60%) and a root mean square error (RMSE) of 9.52 pixels (7.29-12.37). Conclusions: This research highlights the potential of AI movement analysis for the early detection of NDDs, providing valuable insights into the motor development of infants at risk.

Clinical gait analysis using video-based pose estimation: Multiple perspectives, clinical populations, and measuring change

Gait dysfunction is common in many clinical populations and often has a profound and deleterious impact on independence and quality of life. Gait analysis is a foundational component of rehabilitation because it is critical to identify and understand the specific deficits that should be targeted prior to the initiation of treatment. Unfortunately, current state-of-the-art approaches to gait analysis (e.g., marker-based motion capture systems, instrumented gait mats) are largely inaccessible due to prohibitive costs of time, money, and effort required to perform the assessments. Here, we demonstrate the ability to perform quantitative gait analyses in multiple clinical populations using only simple videos recorded using low-cost devices (tablets). We report four primary advances: 1) a novel, versatile workflow that leverages an open-source human pose estimation algorithm (OpenPose) to perform gait analyses using videos recorded from multiple different perspectives (e.g., frontal, sagittal), 2) validation of this workflow in three different populations of participants (adults without gait impairment, persons post-stroke, and persons with Parkinson's disease) via comparison to ground-truth three-dimensional motion capture, 3) demonstration of the ability to capture clinically relevant, condition-specific gait parameters, and 4) tracking of within-participant changes in gait, as is required to measure progress in rehabilitation and recovery. Importantly, our workflow has been made freely available and does not require prior gait analysis expertise. The ability to perform quantitative gait analyses in nearly any setting using only low-cost devices and computer vision offers significant potential for dramatic improvement in the accessibility of clinical gait analysis across different patient populations.

Exploring the State of Machine Learning and Deep Learning in Medicine: A Survey of the Italian Research Community

Artificial intelligence (AI) is becoming increasingly important, especially in the medical field. While AI has been used in medicine for some time, its growth in the last decade is remarkable. Specifically, machine learning (ML) and deep learning (DL) techniques in medicine have been increasingly adopted due to the growing abundance of health-related data, the improved suitability of such techniques for managing large datasets, and more computational power. ML and DL methodologies are fostering the development of new “intelligent” tools and expert systems to process data, to automatize human–machine interactions, and to deliver advanced predictive systems that are changing every aspect of the scientific research, industry, and society. The Italian scientific community was instrumental in advancing this research area. This article aims to conduct a comprehensive investigation of the ML and DL methodologies and applications used in medicine by the Italian research community in the last five years. To this end, we selected all the papers published in the last five years with at least one of the authors affiliated to an Italian institution that in the title, in the abstract, or in the keywords present the terms “machine learning” or “deep learning” and reference a medical area. We focused our research on journal papers under the hypothesis that Italian researchers prefer to present novel but well-established research in scientific journals. We then analyzed the selected papers considering different dimensions, including the medical topic, the type of data, the pre-processing methods, the learning methods, and the evaluation methods. As a final outcome, a comprehensive overview of the Italian research landscape is given, highlighting how the community has increasingly worked on a very heterogeneous range of medical problems.