Energy-Based Metrics for Arthroscopic Skills Assessment.

Minimally invasive skills assessment methods are essential in developing efficient surgical simulators and implementing consistent skills evaluation. Although numerous methods have been investigated in the literature, there is still a need to further improve the accuracy of surgical skills assessment. Energy expenditure can be an indication of motor skills proficiency. The goals of this study are to develop objective metrics based on energy expenditure, normalize these metrics, and investigate classifying trainees using these metrics. To this end, different forms of energy consisting of mechanical energy and work were considered and their values were divided by the related value of an ideal performance to develop normalized metrics. These metrics were used as inputs for various machine learning algorithms including support vector machines (SVM) and neural networks (NNs) for classification. The accuracy of the combination of the normalized energy-based metrics with these classifiers was evaluated through a leave-one-subject-out cross-validation. The proposed method was validated using 26 subjects at two experience levels (novices and experts) in three arthroscopic tasks. The results showed that there are statistically significant differences between novices and experts for almost all of the normalized energy-based metrics. The accuracy of classification using SVM and NN methods was between 70% and 95% for the various tasks. The results show that the normalized energy-based metrics and their combination with SVM and NN classifiers are capable of providing accurate classification of trainees. The assessment method proposed in this study can enhance surgical training by providing appropriate feedback to trainees about their level of expertise and can be used in the evaluation of proficiency.

Randomized control trial for evaluation of a hands-free pointer for surgical instruction during laparoscopic cholecystectomy.

INTRODUCTION: Training surgeons in minimally invasive surgery (MIS) requires surgical residents to operate under the direction of a consultant. The inability of the instructing surgeon to point at the laparoscopic monitor without releasing the instruments remains a barrier to effective instruction. The wireless hands-free surgical pointer (WHaSP) has been developed to aid instruction during MIS. METHODS: The objective of this study was to evaluate the effectiveness and likeability of the WHaSP as an instructional tool compared with the conventional methods. Data were successfully collected during 103 laparoscopic cholecystectomy procedures, which had been randomized to use or not use the WHaSP as a teaching tool. Audio and video from the surgeries were recorded and analyzed. Instructing surgeons, operating surgeons, and camera assistants provided feedback through a post-operative questionnaire that used a five-level Likert scale. The questionnaire results were analyzed using a Mann-Whitney U test. RESULTS: There were no negative effects on surgery completion time or instruction practice due to the use of the WHaSP. The number of times an instructor surgeon pointed to the laparoscopic screen with their hand was significantly reduced when the WHaSP was utilized (p < 0.001). The questionnaires showed that WHaSP users found it to be comfortable, easy to use, and easy to control. Compared to when the WHaSP was not used, users found that communication was more effective (p = 0.002), locations were easier to communicate (p < 0.001), and instructions were easier to follow (p = 0.005). CONCLUSIONS: The WHaSP system was successfully used in surgery. It integrated seamlessly into existing equipment within the operating room and did not affect flow. The positive outcomes of utilizing the WHaSP were improved communication in the OR, improved efficiency and safety of the surgery, easy to use, and comfortable to wear. The surgeons showed a preference for utilizing the WHaSP if given a choice.

Development of force-based metrics for skills assessment in minimally invasive surgery.

BACKGROUND: The loss of haptic information that results from the reduced-access conditions present in minimally invasive surgery (MIS) may compromise the safety of the procedures. This limitation must be overcome through training. However, current methods for determining the skill level of trainees do not measure critical elements of skill attainment. This study aimed to evaluate the usefulness of force information for the assessment of skill during MIS. METHODS: To achieve the study goal, experiments were performed using a set of sensorized instruments capable of measuring instrument position and tissue interaction forces. Several force-based metrics were developed as well as metrics that combine force and position information. RESULTS: The results show that experience level has a strong correlation with the new force-based metrics presented in this article. In particular, the integral and the derivative of the forces or the metrics that combine force and position provide the strongest correlations. CONCLUSIONS: This study showed that force-based metrics are better indications of performance than metrics based on task completion time or position information alone. The proposed metrics can be automatically computed, are completely objective, and measure important aspects of performance.

Force Control Issues in Upper and Lower Limbs in Parkinson's Disease and Freezing of Gait.

Parkinson's Disease (PD) has been found to cause force control deficits in upper and lower limbs. About 50% of patients with advanced PD develop a debilitating symptom called freezing of gait (FOG), which has been linked to force control problems in the lower limbs, and some may only have a limited response to the gold standard pharmaceutical therapy, levodopa, resulting in partially levodopa-responsive FOG (PLR-FOG). There has been limited research on investigating upper-limb force control in people with PD with PLR-FOG, and without FOG. In this pilot study, force control was explored using an upper-and-lower-limb haptics-enabled robot in a reaching task while people with PD with and without PLR-FOG were on their levodopa medication. A healthy control group was used for reference, and each cohort completed the task at three different levels of assistance provided by the robot. Similar significant proportional force control deficits were found in the upper and lower limbs in patients with PLR-FOG versus those without FOG. Some aspects of force control were found to be retained, including an ability to increase or decrease force in response to changes in resistance while completing a reaching task. Overall, these results suggest there are force control deficits in both the upper and lower limbs in people with PLR-FOG.

Robotics-Based Characterization of Sensorimotor Integration in Parkinson's Disease and the Effect of Medication.

Integration of multi-modal sensory inputs and modulation of motor outputs based on perceptual estimates is called Sensorimotor Integration (SMI). Optimal functioning of SMI is essential for perceiving the environment, modulating the motor outputs, and learning or modifying motor skills to suit the demands of the environment. Growing evidence suggests that patients diagnosed with Parkinson's Disease (PD) may suffer from an impairment in SMI that contributes to perceptual deficits, leading to motor abnormalities. However, the exact nature of the SMI impairment is still unclear. This study uses a robot-assisted assessment tool to quantitatively characterize SMI impairments in PD patients and how they affect voluntary movements. A set of assessment tasks was developed using a robotic manipulandum equipped with a virtual-reality system. The sensory conditions of the virtual environment were varied to facilitate the assessment of SMI. A hundred PD patients (before and after medication) and forty-three control subjects completed the tasks under varying sensory conditions. The kinematic measures obtained from the robotic device were used to evaluate SMI. The findings reveal that across all sensory conditions, PD patients had 36% higher endpoint error, 38% higher direction error in reaching tasks, and 43% higher number of violations in tracing tasks than control subjects due to impairment in integrating sensory inputs. However, they still retained motor learning ability and the ability to modulate motor outputs. The medication worsened the SMI deficits as PD patients, after medication, performed worse than before medication when encountering dynamic sensory environments and exhibited impaired motor learning ability.

Robot-assisted investigation of sensorimotor control in Parkinson's disease.

Sensorimotor control (SMC) is a complex function that involves sensory, cognitive, and motor systems working together to plan, update and execute voluntary movements. Any abnormality in these systems could lead to deficits in SMC, which would negatively impact an individual's ability to execute goal-directed motions. Recent studies have shown that patients diagnosed with Parkinson's disease (PD) have dysfunctions in sensory, motor, and cognitive systems, which could give rise to SMC deficits. However, SMC deficits in PD and how they affect a patient's upper-limb movements have not been well understood. The objective of the study was to investigate SMC deficits in PD and how they affect the planning and correction of upper-limb motions. This was accomplished using a robotic manipulandum equipped with a virtual-reality system. Twenty age-matched healthy controls and fifty-six PD patients (before and after medication) completed an obstacle avoidance task under dynamic conditions (target and obstacles in moving or stationary form, with and without mechanical perturbations). Kinematic information from the robot was used to extract eighteen features that evaluated the SMC functions of the participants. The findings show that the PD patients before medication were 32% slower, reached 16% fewer targets, hit 41% more obstacles, and were 26% less efficient than the control participants, and the difference in these features was statistically significant under dynamic conditions. In addition to the motor deficits, the PD patients also showed deficits in handling high cognitive loads and interpreting sensory cues. Further, the PD patients after medication exhibited worse sensory and cognitive performance than before medication under complex testing conditions. The PD patients also showed deficits in following the computational models leading to poor motor planning.

Visual velocity perception dysfunction in Parkinson's disease.

OBJECTIVE: Compared with motor deficits, sensory information processing in Parkinson's disease (PD) is relatively unexplored. While there is increasing interest in understanding the sensory manifestations of PD, the extent of sensory abnormality in PD has remained relatively unexplored. Furthermore, most investigations on the sensory aspects of PD involve motor aspects, causing confounding results. As sensory deficits often arise in early PD development stages, they present a potential technological target for diagnosis and disease monitoring that is affordable and accessible. Considering this, the current study's aim is to assess visual spatiotemporal perception independent of goal directed movements in PD by designing and using a scalable computational tool. METHODS: A flexible 2-D virtual reality environment was created to evaluate various cases of visual perception. Using the tool, an experimental task quantifying the visual perception of velocity was tested on 37 individuals with PD and 17 age-matched control participants. RESULTS: PD patients, both ON and OFF PD therapy, displayed perceptual impairments (p = 0.001 and p = 0.008, respectively) at slower tested velocity magnitudes. These impairments were even observed in early stages of PD (p = 0.015). CONCLUSION: Visual velocity perception is impaired in PD patients, which suggests impairments in visual spatiotemporal processing occur in PD and provides a promising modality to be used with disease monitoring software. SIGNIFICANCE: Visual velocity perception shows high sensitivity to PD at all stages of the disease. Dysfunction in visual velocity perception may contribute to observed motor dysfunction in PD.

Image-guided surgical planning of percutaneous nephrolithotomy with patient-specific CTRs.

PURPOSE: We propose the utilization of patient-specific concentric-tube robots (CTRs) whose designs are optimized to enhance their volumetric reachability of the renal stone, thus reducing the morbidities associated with percutaneous nephrolithotomy procedures. By employing a nested optimization-driven scheme, this work aims to determine a single surgical tract through which the patient-tailored CTR is deployed. We carry out a sensitivity analysis on the combined percutaneous access and optimized CTR design with respect to breathing-induced excursion of the kidneys based on preoperative images. Further, an investigation is also performed of the appropriateness and effectiveness of the percutaneous access provided by the proposed algorithm compared to that of an expert urologist. METHODS: The method is based on an ellipsoidal approximation to the renal calculi and a grid search over candidate skin areas and available renal calyces using an anatomically constrained kinematic mapping of the CTR. Percutaneous access is selected for collision-free CTR deployment to the centroid of the stones with minimal positional error at the renal calyx. Further optimization of the CTR design results in a robot tailored to the therapeutic anatomical features of each clinical case. The study examined 14 sets of clinical data of PCNL patients, analyzing stone reachability using preoperative images and breathing-induced motions of the kidney. An experienced urologist qualitatively assessed the adequacy of percutaneous access generated by the algorithm. RESULTS: An assessment conducted by an expert urologist found that the percutaneous accesses produced by the proposed approach were found to be comparable to those chosen by the expert surgeon in most clinical cases. The simulated results demonstrated a mean volume coverage of [Formula: see text] for static anatomy and [Formula: see text] and [Formula: see text] when considering a 1 cm excursion of the kidney in the craniocaudal directions due to respiration or tool-tissue interaction. CONCLUSION: The optimization-driven scheme for determining a single tract surgical plan, coupled with the use of a patient-specific CTR, shows promising results for improving percutaneous access in PCNL procedures. This approach clearly shows the potential for enhancing the quality and suitability of percutaneous accesses, addressing the challenges posed by staghorn and non-staghorn stones during PCNL procedures. Further research involving clinical validation is necessary to confirm these findings and explore the potential clinical benefits of the approach.

Force sensing-based simulator for arthroscopic skills assessment in orthopaedic knee surgery.

The complexity of knee arthroscopy makes it difficult to teach and assess skill level during real surgery. Simulator-based training is ideal for this complex procedure. To address the limitations of existing systems, a physical simulator, capable of providing skills assessment and feedback has been developed. The simulator measures the forces applied on the femur and acting on the tools. An experimental evaluation was conducted to assess the differences in task completion time and applied forces for fourteen tasks performed by trainees and expert surgeons. Initial results show that the simulator, together with well-chosen tasks, can potentially be used to assess user performance.

Video Context Improves Performance in Identifying Operative Planes on Static Surgical Images.

BACKGROUND: Correct identification of the surgical tissue planes of dissection is paramount at the operating room, and the needed skills seem to be improved with realistic dynamic models rather than mere still images. The objective is to assess the role of adding video prequels to still images taken from operations on the precision and accuracy of tissue plane identification using a validated simulation model, considering various levels of surgeons' experience. METHODS: A prospective observational study was conducted involving 15 surgeons distributed to three equal groups, including a consultant group [C], a senior group [S], and a junior group [J]. Subjects were asked to identify and draw ideal tissue planes in 20 images selected at suitable operative moments of identification before and after showing a 10- second videoclip preceding the still image. A validated comparative metric (using a modified Hausdorff distance [%Hdu] for object matching) was used to measure the distance between lines. A precision analysis was carried out based on the difference in %Hdu between lines drawn before and after watching the videos, and between-group comparisons were analyzed using a one-way analysis of variance (ANOVA). The analysis of accuracy was done on the difference in %Hdu between lines drawn by the subjects and the ideal lines provided by an expert panel. The impact of videos on accuracy was assessed using a repeated-measures ANOVA. RESULTS: The C group showed the highest preciseness as compared to the S and J groups (mean Hdu 9.17±11.86 versus 12.1±15.5 and 20.0±18.32, respectively, p <0.001) and significant differences between groups were found in 14 images (70%). Considering the expert panel as a reference, the interaction between time and experience level was significant ( F (2, 597) = 4.52, p <0.001). Although the subjects of the J group were significantly less accurate than other surgeons, only this group showed significant improvements in mean %Hdu values after watching the lead-in videos ( F (1, 597) = 6.04, p = 0.014). CONCLUSIONS: Adding video context improved the ability of junior trainees to identify tissue planes of dissection. A realistic model is recommended considering experience-based differences in precision in training programs.

CT image construction of a totally deflated lung using deformable model extrapolation.

PURPOSE: A novel technique is proposed to construct CT image of a totally deflated lung from a free-breathing 4D-CT image sequence acquired preoperatively. Such a constructed CT image is very useful in performing tumor ablative procedures such as lung brachytherapy. Tumor ablative procedures are frequently performed while the lung is totally deflated. Deflating the lung during such procedures renders preoperative images ineffective for targeting the tumor. Furthermore, the problem cannot be solved using intraoperative ultrasound (U.S.) images because U.S. images are very sensitive to small residual amount of air remaining in the deflated lung. One possible solution to address these issues is to register high quality preoperative CT images of the deflated lung with their corresponding low quality intraoperative U.S. images. However, given that such preoperative images correspond to an inflated lung, such CT images need to be processed to construct CT images pertaining to the lung's deflated state. METHODS: To obtain the CT images of deflated lung, we present a novel image construction technique using extrapolated deformable registration to predict the deformation the lung undergoes during full deflation. The proposed construction technique involves estimating the lung's air volume in each preoperative image automatically in order to track the respiration phase of each 4D-CT image throughout a respiratory cycle; i.e., the technique does not need any external marker to form a respiratory signal in the process of curve fitting and extrapolation. The extrapolated deformation field is then applied on a preoperative reference image in order to construct the totally deflated lung's CT image. The technique was evaluated experimentally using ex vivo porcine lung. RESULTS: The ex vivo lung experiments led to very encouraging results. In comparison with the CT image of the deflated lung we acquired for the purpose of validation, the constructed CT image was very similar. The intensity mean absolute difference between these two images was calculated to be at 1%. Tumor center as well as a number of anatomical fiducial markers were traced in different corresponding slices of the two images. The average misalignment obtained for the constructed CT image was (0.64, 0.39, 0.11) mm, which indicates a very desirable accuracy for lung brachytherapy applications. CONCLUSIONS: The image construction accuracy obtained in this research is suitable for intraoperative tasks; e.g., tumor localization and fusing with real time navigation data in lung brachytherapy. These applications involve image registration with intraoperative U.S. images in order to enhance their poor quality. The proposed technique is also useful for preoperative tasks such as planning of lung brachytherapy treatment.

Force sensing in natural orifice transluminal endoscopic surgery.

BACKGROUND: Natural orifice transluminal endoscopic surgery (NOTES) may represent the next frontier for therapeutic minimally invasive surgery; however, its feasibility is currently limited by the lack of suitable instruments. Identifying the forces required to manipulate tissue during NOTES is a necessary first step in the development of better instrumentation. METHODS: Sensorized instruments were used to measure the forces acting at the tip of the instruments during transgastric and transperineal NOTES procedures performed in two female pigs. The maximum and average forces when handling tissue were determined and compared. RESULTS: The results show that, for the transgastric approach, the average forces required are significantly less than in the transperineal approach (43% less), and that the maximum forces required are almost 8 and 16 N in the transgastric and transperineal approaches, respectively. The forces were higher than 5 N in 1.6% of the measurements in the transgastric approach and 2.9% in the transperineal approach. CONCLUSIONS: This study presents an experimental measurement of tissue manipulation forces in a NOTES procedure. This information may be valuable for research groups interested in developing NOTES instruments and devices. It is recommended that NOTES instruments be designed to easily handle forces as high as 16 N.

Texture-based Intraoperative Image Guidance for Tumor Localization in Minimally Invasive Surgery.

Intraoperative tumor localization in a deflated lung in minimally invasive surgery (MIS) is challenging as the lung cannot be manually palpated through small incisions. To do so remotely, an articulated multisensory imaging device combining tactile and ultrasound sensors was developed. It visualizes the surface tactile map and the depth of the tissue. However, with little maneuverability in MIS, localizing tumors using instrumented palpation is both tedious and inefficient. In this paper, a texture- based image guidance system that classifies tactile-guided ultrasound texture regions and provides beliefs on their types is proposed. The resulting interactive feedback allows directed palpation in MIS. A k-means classifier is used to first cluster gray-level co-occurrence matrix (GLCM)-based texture features of the ultrasound regions, followed by hidden Markov model-based belief propagation to establish confidence about the clustered features observing repeated patterns. When the beliefs converge, the system autonomously detects tumor and nontumor textures. The approach was tested on 20 ex vivo soft tissue specimens in a staged MIS. The results showed that with guidance, tumors in MIS could be localized with 98% accuracy, 99% sensitivity, and 97% specificity.Clinical Relevance- Texture-based image guidance adds efficiency and control to instrumented palpation in MIS. It renders fluidity and accuracy in image acquisition using a hand-held device where fatigue from prolonged handling affects imaging quality.

Abnormal Vision-Based Displacement Perception in Parkinson's Disease.

In this work, we investigate the effect of Parkinson's disease (PD), and common corresponding therapies on vision-based perception of motion, a critical perceptual ability required for performing a wide range of activities of daily livings. While PD has been recognized as mainly a motor disorder, sensory manifestation of PD can also play a major role in the resulting disability. In this paper, for the first time, the effect of disease duration and common therapies on vision-based perception of displacement were investigated. The study is conducted in a movement-independent manner, to reject the shadowing effects and isolate the targeted perceptual disorder to the maximum possible extent. Data was collected using a computerized graphical tool on 37 PD patients [6 early-stage de novo, 25 mid-stage using levodopa therapy, six later-stage using deep brain stimulation (DBS)] and 15 control participants. Besides the absolute measurement of perception through a psychometric analysis on two tested position reference magnitudes, we also investigated the linearity in perception using Weber's fraction. The results showed that individuals with PD displayed significant perceptual impairments compared to controls, though early-stage patients were not impaired. Mid-stage patients displayed impairments at the greater of the two tested reference magnitudes, while late-stage patients were impaired at both reference magnitudes. Levodopa and DBS use did not cause statistically significant differences in absolute displacement perception. The findings suggest abnormal visual processing in PD increasing with disease development, perhaps contributing to sensory-based impairments of PD such as bradykinesia, visuospatial deficits, and abnormal object recognition.

Robotics and AI for Teleoperation, Tele-Assessment, and Tele-Training for Surgery in the Era of COVID-19: Existing Challenges, and Future Vision.

The unprecedented shock caused by the COVID-19 pandemic has severely influenced the delivery of regular healthcare services. Most non-urgent medical activities, including elective surgeries, have been paused to mitigate the risk of infection and to dedicate medical resources to managing the pandemic. In this regard, not only surgeries are substantially influenced, but also pre- and post-operative assessment of patients and training for surgical procedures have been significantly impacted due to the pandemic. Many countries are planning a phased reopening, which includes the resumption of some surgical procedures. However, it is not clear how the reopening safe-practice guidelines will impact the quality of healthcare delivery. This perspective article evaluates the use of robotics and AI in 1) robotics-assisted surgery, 2) tele-examination of patients for pre- and post-surgery, and 3) tele-training for surgical procedures. Surgeons interact with a large number of staff and patients on a daily basis. Thus, the risk of infection transmission between them raises concerns. In addition, pre- and post-operative assessment also raises concerns about increasing the risk of disease transmission, in particular, since many patients may have other underlying conditions, which can increase their chances of mortality due to the virus. The pandemic has also limited the time and access that trainee surgeons have for training in the OR and/or in the presence of an expert. In this article, we describe existing challenges and possible solutions and suggest future research directions that may be relevant for robotics and AI in addressing the three tasks mentioned above.

A deep explainable artificial intelligent framework for neurological disorders discrimination.

Pathological hand tremor (PHT) is a common symptom of Parkinson's disease (PD) and essential tremor (ET), which affects manual targeting, motor coordination, and movement kinetics. Effective treatment and management of the symptoms relies on the correct and in-time diagnosis of the affected individuals, where the characteristics of PHT serve as an imperative metric for this purpose. Due to the overlapping features of the corresponding symptoms, however, a high level of expertise and specialized diagnostic methodologies are required to correctly distinguish PD from ET. In this work, we propose the data-driven [Formula: see text] model, which processes the kinematics of the hand in the affected individuals and classifies the patients into PD or ET. [Formula: see text] is trained over 90 hours of hand motion signals consisting of 250 tremor assessments from 81 patients, recorded at the London Movement Disorders Centre, ON, Canada. The [Formula: see text] outperforms its state-of-the-art counterparts achieving exceptional differential diagnosis accuracy of [Formula: see text]. In addition, using the explainability and interpretability measures for machine learning models, clinically viable and statistically significant insights on how the data-driven model discriminates between the two groups of patients are achieved.

Suitability of three saws for minimally invasive bone cutting.

This study compares 3 different saw types to determine which is best suited for integration into a minimally invasive bone saw. A handheld electric jigsaw, a coping saw, and a Gigli saw were used to cut into porcine ilium. Heat generated was measured using a thermocouple, and forces applied during cutting were recorded using a force/torque sensor. The coping saw generated an average maximum temperature that was 26 degrees C less than that generated using the jigsaw (P < .001) and 14 degrees C less than that for the Gigli saw (P < .001). On average, the maximum force applied through the coping saw was 14 N less than that through the jigsaw (P < .001) and 18 N less than that through the Gigli saw (P < .001). Out of the 3 saws tested, the coping saw is optimal for cutting bone based on heat generation and required force.

Intraoperative Localization of STN During DBS Surgery Using a Data-Driven Model.

A new approach is presented for localizing the Subthalamic Nucleus (STN) during Deep Brain Stimulation (DBS) surgery based on microelectrode recordings (MERs). DBS is an accepted treatment for individuals living with Parkinson's Disease (PD). This surgery involves implantation of a permanent electrode inside the STN to deliver electrical current. Since the STN is a very small region inside the brain, accurate placement of an electrode is a challenging task for the surgical team. Prior to placement of the permanent electrode, microelectrode recordings of brain activity are used intraoperatively to localize the STN. The placement of the electrode and the success of the therapy depend on this location. In this paper, an objective approach is implemented to help the surgical team in localizing the STN. This is achieved by processing the MER signals and extracting features during the surgery to be used in a Machine Learning (ML) algorithm for defining the neurophysiological borders of the STN. For this purpose, a new classification approach is proposed with the goal of detecting both the dorsal and the ventral borders of the STN during the surgical procedure. Results collected from 100 PD patients in this study, show that by calculating and extracting wavelet transformation features from MER signals and using a data-driven computational deep neural network model, it is possible to detect the borders of the STN with an accuracy of 92%. The proposed method can be implemented in real-time during the surgery to model the neurophysiological nonlinearity along the path of the electrode trajectory during insertion.

PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor using Recurrent Neural Network Models.

The global aging phenomenon has increased the number of individuals with age-related neurological movement disorders including Parkinson's Disease (PD) and Essential Tremor (ET). Pathological Hand Tremor (PHT), which is considered among the most common motor symptoms of such disorders, can severely affect patients' independence and quality of life. To develop advanced rehabilitation and assistive technologies, accurate estimation/prediction of nonstationary PHT is critical, however, the required level of accuracy has not yet been achieved. The lack of sizable datasets and generalizable modeling techniques that can fully represent the spectrotemporal characteristics of PHT have been a critical bottleneck in attaining this goal. This paper addresses this unmet need through establishing a deep recurrent model to predict and eliminate the PHT component of hand motion. More specifically, we propose a machine learning-based, assumption-free, and real-time PHT elimination framework, the PHTNet, by incorporating deep bidirectional recurrent neural networks. The PHTNet is developed over a hand motion dataset of 81 ET and PD patients collected systematically in a movement disorders clinic over 3 years. The PHTNet is the first intelligent systems model developed on this scale for PHT elimination that maximizes the resolution of estimation and allows for prediction of future and upcoming sub-movements.

Differential Temporal Perception Abilities in Parkinson's Disease Patients Based on Timing Magnitude.

Non-motor symptoms in Parkinson's Disease (PD) predate motor symptoms and substantially decrease quality of life; however, detection, monitoring, and treatments are unavailable for many of these symptoms. Temporal perception abnormalities in PD are generally attributed to altered Basal Ganglia (BG) function. Present studies are confounded by motor control facilitating movements that are integrated into protocols assessing temporal perception. There is uncertainty regarding the BG's influence on timing processes of different time scales and how PD therapies affect this perception. In this study, PD patients using Levodopa (n = 25), Deep Brain Stimulation (DBS; n = 6), de novo patients (n = 6), and healthy controls (n = 17) completed a visual temporal perception task in seconds and sub-section timing scales using a computer-generated graphical tool. For all patient groups, there were no impairments seen at the smaller tested magnitudes (using sub-second timing). However, all PD groups displayed significant impairments at the larger tested magnitudes (using interval timing). Neither Levodopa nor DBS therapy led to significant improvements in timing abilities. Levodopa resulted in a strong trend towards impairing timing processes and caused a deterioration in perceptual coherency according to Weber's Law. It is shown that timing abnormalities in PD occur in the seconds range but do not extend to the sub-second range. Furthermore, observed timing deficits were shown to not be solely caused by motor deficiency. This provides evidence to support internal clock models involving the BG (among other neural regions) in interval timing, and cerebellar control of sub-second timing. This study also revealed significant temporal perception deficits in recently diagnosed PD patients; thus, temporal perception abnormalities might act as an early disease marker, with the graphical tool showing potential for disease monitoring.