Machine learning and wearable sensors at preoperative assessments: Functional recovery prediction to set realistic expectations for knee replacements.

Unmet expectations contribute to a high patient dissatisfaction rate following total knee replacement but clinicians currently do not have the tools to confidently adjust expectations. In this study, supervised machine learning was applied to multi-variate wearable sensor data from preoperative timed-up-and-go tests. Participants (n=82) were instrumented three months after surgery and patients showing relevant improvement were designated as "responders" while the remainder were labelled "maintainers". Support vector machine, naïve Bayes, and random forest binary classifiers were developed to distinguish patients using sensor-derived features. Accuracy, sensitivity, specificity, and area under the receiver-operator curve (AUC) were compared between models using ten-fold out-of-sample testing. A high performance using only sensor-derived functional metrics was obtained with a random forest model (accuracy = 0.76 ± 0.11, sensitivity = 0.87 ± 0.08, specificity = 0.57 ± 0.26, AUC = 0.80 ± 0.14) but highly sensitive models were observed using naïve Bayes and SVM models after including patient age, sex, and BMI into the feature set (accuracy = 0.72, 0.73 ± 0.09, 0.12; sensitivity = 0.94, 0.95 ± 0.11, 0.11; specificity = 0.35, 0.37 ± 0.20, 0.18; AUC = 0.80, 0.74 ± 0.07, 0.11; respectfully). Including select patient-reported subjective measures increased the top random forest performance slightly (accuracy = 0.80 ± 0.10, sensitivity = 0.91 ± 0.14, specificity = 0.62 ± 0.23, AUC = 0.86 ± 0.09). The current work has demonstrated that prediction models developed from preoperative sensor-derived functional metrics can reliably predict expected functional recovery following surgery and this can be used by clinicians to help set realistic patient expectations.

Machine Learning Groups Patients by Early Functional Improvement Likelihood Based on Wearable Sensor Instrumented Preoperative Timed-Up-and-Go Tests.

BACKGROUND: Wearable sensors permit efficient data collection and unobtrusive systems can be used for instrumenting knee patients for objective assessment. Machine learning can be leveraged to parse the abundant information these systems provide and segment patients into relevant groups without specifying group membership criteria. The objective of this study is to examine functional parameters influencing favorable recovery outcomes by separating patients into functional groups and tracking them through clinical follow-ups. METHODS: Patients undergoing primary unilateral total knee arthroplasty (n = 68) completed instrumented timed-up-and-go tests preoperatively and at their 2-, 6-, and 12-week follow-up appointments. A custom wearable system extracted 55 metrics for analysis and a K-means algorithm separated patients into functionally distinguished groups based on the derived features. These groups were analyzed to determine which metrics differentiated most and how each cluster improved during early recovery. RESULTS: Patients separated into 2 clusters (n = 46 and n = 22) with significantly different test completion times (12.6 s vs 21.6 s, P < .001). Tracking the recovery of both groups to their 12-week follow-ups revealed 64% of one group improved their function while 63% of the other maintained preoperative function. The higher improvement group shortened their test times by 4.94 s, (P = .005) showing faster recovery while the other group did not improve above a minimally important clinical difference (0.87 s, P = .07). Features with the largest effect size between groups were distinguished as important functional parameters. CONCLUSION: This work supports using wearable sensors to instrument functional tests during clinical visits and using machine learning to parse complex patterns to reveal clinically relevant parameters.

Proposal and Validation of a Knee Measurement System for Patients With Osteoarthritis.

OBJECTIVE: Currently most measurements of knee joint function are obtained through observation and patient-reported outcomes. This paper proposes an implementation and validation of a knee monitor to measure quantitative joint data in multiple degrees of freedom. The proposed system is configurable with minimal patient interaction and no frame-alignment calibration procedure is required for measurement after visually placing/replacing sensors on patients. METHODS: A mobile software system was developed using a method of extracting clinical knee angles based on attitude estimations from independent wearable sensors. Validation was performed using a robot phantom and results were compared with a gold standard motion capture system. Two instrumentation placements (lateral and posterior) were examined. RESULTS: A posterior sensor placement was determined to provide the most repeatable results through multiple degrees of freedom and measurement accuracy approached a gold standard motion capture technology with low root-mean-square error (flexion: 3.34°, internal/external rotation: 2.18°, and varus/valgus: 1.44°). CONCLUSION: The proposed system is simple to use and convenient for use in ambulatory or unsupervised environments for joint measurement; however, it was shown that accuracy can be sensitive to sensor placement. SIGNIFICANCE: This system would be beneficial for obtaining quantitative patient data or tracking functional activity in variable environments, providing clinicians with indications of how patients' knees function during activity, potentially permitting more individualized care and recommendations.

Dynamic 3-D virtual fixtures for minimally invasive beating heart procedures.

Two-dimensional or 3-D visual guidance is often used for minimally invasive cardiac surgery and diagnosis. This visual guidance suffers from several drawbacks such as limited field of view, loss of signal from time to time, and in some cases, difficulty of interpretation. These limitations become more evident in beating-heart procedures when the surgeon has to perform a surgical procedure in the presence of heart motion. In this paper, we propose dynamic 3-D virtual fixtures (DVFs) to augment the visual guidance system with haptic feedback, to provide the surgeon with more helpful guidance by constraining the surgeon's hand motions thereby protecting sensitive structures. DVFs can be generated from preoperative dynamic magnetic resonance (MR) or computed tomograph (CT) images and then mapped to the patient during surgery. We have validated the feasibility of the proposed method on several simulated surgical tasks using a volunteer's cardiac image dataset. Validation results show that the integration of visual and haptic guidance can permit a user to perform surgical tasks more easily and with reduced error rate. We believe this is the first work presented in the field of virtual fixtures that explicitly considers heart motion.

A potential field model using generalized sigmoid functions.

The lack of a potential field model capable of providing accurate representations of objects of arbitrary shapes is considered one major limitation in applying the artificial potential field method in many practical applications. In this correspondence, we propose a potential function based on generalized sigmoid functions. The generalized sigmoid model can be constructed from combinations of implicit primitives or from sampled surface data. The constructed potential field model can achieve an accurate analytic description of objects in two or three dimensions and requires very modest computation at run time. In this correspondence, applications of the generalized sigmoid model in path-planning tasks for mobile robots and in haptic feedback tasks are presented. The validation results in this correspondence show that the model can effectively allow the user or mobile robot to avoid penetrations of obstacles while successfully accomplishing the task.

Rendering of virtual fixtures for MIS using generalized sigmoid functions.

To avoid undesired collisions and improve the level of safety and precision, artificial potential field (APF) can be employed to generate virtual forces around protected tissue and to provide surgeons with real-time force refection through haptic feedback. In this paper, we propose a potential field-based force model using the generalized sigmoid function, and show that it can represent a large class of shapes. The proposed approach has several advantages such as computational efficiency, easily adjustable level of force reflection, and force continuity.