Artificial intelligence meets medical robotics.

Artificial intelligence (AI) applications in medical robots are bringing a new era to medicine. Advanced medical robots can perform diagnostic and surgical procedures, aid rehabilitation, and provide symbiotic prosthetics to replace limbs. The technology used in these devices, including computer vision, medical image analysis, haptics, navigation, precise manipulation, and machine learning (ML) , could allow autonomous robots to carry out diagnostic imaging, remote surgery, surgical subtasks, or even entire surgical procedures. Moreover, AI in rehabilitation devices and advanced prosthetics can provide individualized support, as well as improved functionality and mobility (see the figure). The combination of extraordinary advances in robotics, medicine, materials science, and computing could bring safer, more efficient, and more widely available patient care in the future. -Gemma K. Alderton.

What we look for at Science Robotics.

Science Robotics welcomes papers demonstrating technical and scientific advances, with potential for influence beyond robotics.

Concepts and Trends n Autonomy for Robot-Assisted Surgery.

Surgical robots have been widely adopted with over 4000 robots being used in practice daily. However, these are telerobots that are fully controlled by skilled human surgeons. Introducing "surgeon-assist"-some forms of autonomy-has the potential to reduce tedium and increase consistency, analogous to driver-assist functions for lanekeeping, cruise control, and parking. This article examines the scientific and technical backgrounds of robotic autonomy in surgery and some ethical, social, and legal implications. We describe several autonomous surgical tasks that have been automated in laboratory settings, and research concepts and trends.

Comparison of novel shielded nasopharynx applicator designs for intracavitary brachytherapy.

PURPOSE: Nasopharyngeal brachytherapy is limited in part by the radiotolerance of nearby organs like the soft palate. This study explores several novel shielding designs for an intracavitary applicator to significantly reduce soft palate dose while adhering to the constraints of standard treatment procedure. METHODS: The Monte Carlo code TOPAS is used to characterize each prototype under typical high-dose-rate treatment conditions. Mucosal surface dose maps are collected to evaluate the shields on their dose reduction to the central and soft palate planning points and uniformity in their shielding profile. Practicality with respect to patient comfort and pretreatment imaging is discussed. History-by-history standard deviations are calculated for each simulation. RESULTS: A design with elliptical tubing containing bundles of tantalum wires provides the most significant attenuation with 39% and 27% dose reduction to the center and soft palate locations, respectively. Another design utilizing miniature lead spheres loaded into a constructed cavity shows 27% and 24% dose reduction to the same locations while providing more uniform shielding and several practical benefits. Both shields are designed to be completely removable for applicator insertion and pretreatment imaging. The mean and maximum standard error of relative dose measurements was 0.36 and 1.14 percentage points, respectively. CONCLUSION: Each shielding design presented in this study provides a novel approach to safely and effectively shield healthy tissue during intracavitary nasopharyngeal brachytherapy. Analysis performed using Monte Carlo suggests that the design using metal spheres most practically shields the soft palate and should be advanced to the next stages of clinical optimization.

Five years of Science Robotics.

Looking back at the last 5 years of Science Robotics and looking forward to the next 5.

Getting a grip on reality.

The ability to reliably grasp and manipulate novel objects is a grand challenge for robotics.

Deep learning can accelerate grasp-optimized motion planning.

Robots for picking in e-commerce warehouses require rapid computing of efficient and smooth robot arm motions between varying configurations. Recent results integrate grasp analysis with arm motion planning to compute optimal smooth arm motions; however, computation times on the order of tens of seconds dominate motion times. Recent advances in deep learning allow neural networks to quickly compute these motions; however, they lack the precision required to produce kinematically and dynamically feasible motions. While infeasible, the network-computed motions approximate the optimized results. The proposed method warm starts the optimization process by using the approximate motions as a starting point from which the optimizing motion planner refines to an optimized and feasible motion with few iterations. In experiments, the proposed deep learning-based warm-started optimizing motion planner reduces compute and motion time when compared to a sampling-based asymptotically optimal motion planner and an optimizing motion planner. When applied to grasp-optimized motion planning, the results suggest that deep learning can reduce the computation time by two orders of magnitude (300×), from 29 s to 80 ms, making it practical for e-commerce warehouse picking.

Combating COVID-19-The role of robotics in managing public health and infectious diseases.

COVID-19 may drive sustained research in robotics to address risks of infectious diseases.

Applying machine learning to predict future adherence to physical activity programs.

BACKGROUND: Identifying individuals who are unlikely to adhere to a physical exercise regime has potential to improve physical activity interventions. The aim of this paper is to develop and test adherence prediction models using objectively measured physical activity data in the Mobile Phone-Based Physical Activity Education program (mPED) trial. To the best of our knowledge, this is the first to apply Machine Learning methods to predict exercise relapse using accelerometer-recorded physical activity data. METHODS: We use logistic regression and support vector machine methods to design two versions of a Discontinuation Prediction Score (DiPS), which uses objectively measured past data (e.g., steps and goal achievement) to provide a numerical quantity indicating the likelihood of exercise relapse in the upcoming week. The respective prediction accuracy of these two versions of DiPS are compared, and then numerical simulation is performed to explore the potential of using DiPS to selectively allocate financial incentives to participants to encourage them to increase physical activity. RESULTS: we had access to a physical activity trial data that were continuously collected every 60 sec every day for 9 months in 210 participants. By using the first 15 weeks of data as training and test on weeks 16-30, we show that both versions of DiPS have a test AUC of 0.9 with high sensitivity and specificity in predicting the probability of exercise adherence. Simulation results assuming different intervention regimes suggest the potential benefit of using DiPS as a score to allocate resources in physical activity intervention programs in reducing costs over other allocation schemes. CONCLUSIONS: DiPS is capable of making accurate and robust predictions for future weeks. The most predictive features are steps and physical activity intensity. Furthermore, the use of DiPS scores can be a promising approach to determine when or if to provide just-in-time messages and step goal adjustments to improve compliance. Further studies on the use of DiPS in the design of physical activity promotion programs are warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT01280812 Registered on January 21, 2011.

Learning ambidextrous robot grasping policies.

Universal picking (UP), or reliable robot grasping of a diverse range of novel objects from heaps, is a grand challenge for e-commerce order fulfillment, manufacturing, inspection, and home service robots. Optimizing the rate, reliability, and range of UP is difficult due to inherent uncertainty in sensing, control, and contact physics. This paper explores "ambidextrous" robot grasping, where two or more heterogeneous grippers are used. We present Dexterity Network (Dex-Net) 4.0, a substantial extension to previous versions of Dex-Net that learns policies for a given set of grippers by training on synthetic datasets using domain randomization with analytic models of physics and geometry. We train policies for a parallel-jaw and a vacuum-based suction cup gripper on 5 million synthetic depth images, grasps, and rewards generated from heaps of three-dimensional objects. On a physical robot with two grippers, the Dex-Net 4.0 policy consistently clears bins of up to 25 novel objects with reliability greater than 95% at a rate of more than 300 mean picks per hour.

Objectively Measured Baseline Physical Activity Patterns in Women in the mPED Trial: Cluster Analysis.

BACKGROUND: Determining patterns of physical activity throughout the day could assist in developing more personalized interventions or physical activity guidelines in general and, in particular, for women who are less likely to be physically active than men. OBJECTIVE: The aims of this report are to identify clusters of women based on accelerometer-measured baseline raw metabolic equivalent of task (MET) values and a normalized version of the METs ≥3 data, and to compare sociodemographic and cardiometabolic risks among these identified clusters. METHODS: A total of 215 women who were enrolled in the Mobile Phone Based Physical Activity Education (mPED) trial and wore an accelerometer for at least 8 hours per day for the 7 days prior to the randomization visit were analyzed. The k-means clustering method and the Lloyd algorithm were used on the data. We used the elbow method to choose the number of clusters, looking at the percentage of variance explained as a function of the number of clusters. RESULTS: The results of the k-means cluster analyses of raw METs revealed three different clusters. The unengaged group (n=102) had the highest depressive symptoms score compared with the afternoon engaged (n=65) and morning engaged (n=48) groups (overall P<.001). Based on a normalized version of the METs ≥3 data, the moderate-to-vigorous physical activity (MVPA) evening peak group (n=108) had a higher body mass index (P=.03), waist circumference (P=.02), and hip circumference (P=.03) than the MVPA noon peak group (n=61). CONCLUSIONS: Categorizing physically inactive individuals into more specific activity patterns could aid in creating timing, frequency, duration, and intensity of physical activity interventions for women. Further research is needed to confirm these cluster groups using a large national dataset. TRIAL REGISTRATION: ClinicalTrials.gov NCT01280812; https://clinicaltrials.gov/ct2/show/NCT01280812 (Archived by WebCite at http://www.webcitation.org/6vVyLzwft).

Evaluating Machine Learning-Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial.

BACKGROUND: Growing evidence shows that fixed, nonpersonalized daily step goals can discourage individuals, resulting in unchanged or even reduced physical activity. OBJECTIVE: The aim of this randomized controlled trial (RCT) was to evaluate the efficacy of an automated mobile phone-based personalized and adaptive goal-setting intervention using machine learning as compared with an active control with steady daily step goals of 10,000. METHODS: In this 10-week RCT, 64 participants were recruited via email announcements and were required to attend an initial in-person session. The participants were randomized into either the intervention or active control group with a one-to-one ratio after a run-in period for data collection. A study-developed mobile phone app (which delivers daily step goals using push notifications and allows real-time physical activity monitoring) was installed on each participant's mobile phone, and participants were asked to keep their phone in a pocket throughout the entire day. Through the app, the intervention group received fully automated adaptively personalized daily step goals, and the control group received constant step goals of 10,000 steps per day. Daily step count was objectively measured by the study-developed mobile phone app. RESULTS: The mean (SD) age of participants was 41.1 (11.3) years, and 83% (53/64) of participants were female. The baseline demographics between the 2 groups were similar (P>.05). Participants in the intervention group (n=34) had a decrease in mean (SD) daily step count of 390 (490) steps between run-in and 10 weeks, compared with a decrease of 1350 (420) steps among control participants (n=30; P=.03). The net difference in daily steps between the groups was 960 steps (95% CI 90-1830 steps). Both groups had a decrease in daily step count between run-in and 10 weeks because interventions were also provided during run-in and no natural baseline was collected. CONCLUSIONS: The results showed the short-term efficacy of this intervention, which should be formally evaluated in a full-scale RCT with a longer follow-up period. TRIAL REGISTRATION: ClinicalTrials.gov: NCT02886871; https://clinicaltrials.gov/ct2/show/NCT02886871 (Archived by WebCite at http://www.webcitation.org/6wM1Be1Ng).

Personalizing Mobile Fitness Apps using Reinforcement Learning.

Despite the vast number of mobile fitness applications (apps) and their potential advantages in promoting physical activity, many existing apps lack behavior-change features and are not able to maintain behavior change motivation. This paper describes a novel fitness app called CalFit, which implements important behavior-change features like dynamic goal setting and self-monitoring. CalFit uses a reinforcement learning algorithm to generate personalized daily step goals that are challenging but attainable. We conducted the Mobile Student Activity Reinforcement (mSTAR) study with 13 college students to evaluate the efficacy of the CalFit app. The control group (receiving goals of 10,000 steps/day) had a decrease in daily step count of 1,520 (SD ± 740) between baseline and 10-weeks, compared to an increase of 700 (SD ± 830) in the intervention group (receiving personalized step goals). The difference in daily steps between the two groups was 2,220, with a statistically significant p = 0.039.

Evaluation of PC-ISO for customized, 3D Printed, gynecologic 192-Ir HDR brachytherapy applicators.

The purpose of this study was to evaluate the radiation attenuation properties of PC-ISO, a commercially available, biocompatible, sterilizable 3D printing material, and its suitability for customized, single-use gynecologic (GYN) brachytherapy applicators that have the potential for accurate guiding of seeds through linear and curved internal channels. A custom radiochromic film dosimetry apparatus was 3D-printed in PC-ISO with a single catheter channel and a slit to hold a film segment. The apparatus was designed specifically to test geometry pertinent for use of this material in a clinical setting. A brachytherapy dose plan was computed to deliver a cylindrical dose distribution to the film. The dose plan used an 192Ir source and was normalized to 1500 cGy at 1 cm from the channel. The material was evaluated by comparing the film exposure to an identical test done in water. The Hounsfield unit (HU) distributions were computed from a CT scan of the apparatus and compared to the HU distribution of water and the HU distribution of a commercial GYN cylinder applicator. The dose depth curve of PC-ISO as measured by the radiochromic film was within 1% of water between 1 cm and 6 cm from the channel. The mean HU was -10 for PC-ISO and -1 for water. As expected, the honeycombed structure of the PC-ISO 3D printing process created a moderate spread of HU values, but the mean was comparable to water. PC-ISO is sufficiently water-equivalent to be compatible with our HDR brachytherapy planning system and clinical workflow and, therefore, it is suitable for creating custom GYN brachytherapy applicators. Our current clinical practice includes the use of custom GYN applicators made of commercially available PC-ISO when doing so can improve the patient's treatment. 

Risk factors for osteonecrosis of the jaws: a case-control study from the CONDOR Dental PBRN.

Case reports and cohort studies have linked bisphosphonate therapy and osteonecrosis of the jaws (ONJ), but neither causality nor specific risks for lesion development have been clearly established. We conducted a 1:3 case-control study with 3 dental practice-based research networks, using dentist questionnaires and patient interviews for collection of data on bisphosphonate therapy, demographics, co-morbidities, and dental and medical treatments. Multivariable logistic regression analyses tested associations between bisphosphonate use and other risk factors with ONJ. We enrolled 191 ONJ cases and 573 controls in 119 dental practices. Bisphosphonate use was strongly associated with ONJ (odds ratios [OR] 299.5 {95% CI 70.0-1282.7} for intravenous [IV] use and OR = 12.2 {4.3-35.0} for oral use). Risk markers included local suppuration (OR = 7.8 {1.8-34.1}), dental extraction (OR = 7.6 {2.4-24.7}), and radiation therapy (OR = 24.1 {4.9-118.4}). When cancer patients (n = 143) were excluded, bisphosphonate use (OR = 7.2 {2.1-24.7}), suppuration (OR = 11.9 {2.0-69.5}), and extractions (OR = 6.6 {1.6-26.6}) remained associated with ONJ. Higher risk of ONJ began within 2 years of bisphosphonate initiation and increased 4-fold after 2 years. Both IV and oral bisphosphonate use were strongly associated with ONJ. Duration of treatment >2 years; suppuration and dental extractions were independent risk factors for ONJ.

NPIP: A skew line needle configuration optimization system for HDR brachytherapy.

PURPOSE: In this study, the authors introduce skew line needle configurations for high dose rate (HDR) brachytherapy and needle planning by integer program (NPIP), a computational method for generating these configurations. NPIP generates needle configurations that are specific to the anatomy of the patient, avoid critical structures near the penile bulb and other healthy structures, and avoid needle collisions inside the body. METHODS: NPIP consisted of three major components: a method for generating a set of candidate needles, a needle selection component that chose a candidate needle subset to be inserted, and a dose planner for verifying that the final needle configuration could meet dose objectives. NPIP was used to compute needle configurations for prostate cancer data sets from patients previously treated at our clinic. NPIP took two user-parameters: a number of candidate needles, and needle coverage radius, δ. The candidate needle set consisted of 5000 needles, and a range of δ values was used to compute different needle configurations for each patient. Dose plans were computed for each needle configuration. The number of needles generated and dosimetry were analyzed and compared to the physician implant. RESULTS: NPIP computed at least one needle configuration for every patient that met dose objectives, avoided healthy structures and needle collisions, and used as many or fewer needles than standard practice. These needle configurations corresponded to a narrow range of δ values, which could be used as default values if this system is used in practice. The average end-to-end runtime for this implementation of NPIP was 286 s, but there was a wide variation from case to case. CONCLUSIONS: The authors have shown that NPIP can automatically generate skew line needle configurations with the aforementioned properties, and that given the correct input parameters, NPIP can generate needle configurations which meet dose objectives and use as many or fewer needles than the current HDR brachytherapy workflow. Combined with robot assisted brachytherapy, this system has the potential to reduce side effects associated with treatment. A physical trial should be done to test the implant feasibility of NPIP needle configurations.

IPIP: A new approach to inverse planning for HDR brachytherapy by directly optimizing dosimetric indices.

PURPOSE: Many planning methods for high dose rate (HDR) brachytherapy require an iterative approach. A set of computational parameters are hypothesized that will give a dose plan that meets dosimetric criteria. A dose plan is computed using these parameters, and if any dosimetric criteria are not met, the process is iterated until a suitable dose plan is found. In this way, the dose distribution is controlled by abstract parameters. The purpose of this study is to develop a new approach for HDR brachytherapy by directly optimizing the dose distribution based on dosimetric criteria. METHODS: The authors developed inverse planning by integer program (IPIP), an optimization model for computing HDR brachytherapy dose plans and a fast heuristic for it. They used their heuristic to compute dose plans for 20 anonymized prostate cancer image data sets from patients previously treated at their clinic database. Dosimetry was evaluated and compared to dosimetric criteria. RESULTS: Dose plans computed from IPIP satisfied all given dosimetric criteria for the target and healthy tissue after a single iteration. The average target coverage was 95%. The average computation time for IPIP was 30.1 s on an Intel(R) Core 2 Duo CPU 1.67 GHz processor with 3 Gib RAM. CONCLUSIONS: IPIP is an HDR brachytherapy planning system that directly incorporates dosimetric criteria. The authors have demonstrated that IPIP has clinically acceptable performance for the prostate cases and dosimetric criteria used in this study, in both dosimetry and runtime. Further study is required to determine if IPIP performs well for a more general group of patients and dosimetric criteria, including other cancer sites such as GYN.

Risk factors for osteonecrosis of the jaws: a case-control study from the CONDOR dental PBRN.

Case reports and cohort studies have linked bisphosphonate therapy and osteonecrosis of the jaws (ONJ), but neither causality nor specific risks for lesion development have been clearly established. We conducted a 1:3 case-control study with three dental Practice-based Research Networks, using dentist questionnaires and patient interviews for collection of data on bisphosphonate therapy, demographics, co-morbidities, and dental and medical treatments. Multivariable logistic regression analyses tested associations between bisphosphonate use and other risk factors with ONJ. We enrolled 191 ONJ cases and 573 controls in 119 dental practices. Bisphosphonate use was strongly associated with ONJ (odds ratios [OR] 299.5 {95%CI 70.0-1282.7} for intravenous [IV] use and OR = 12.2 {4.3-35.0} for oral use). Risk markers included local suppuration (OR = 7.8 {1.8-34.1}), dental extraction (OR = 7.6 {2.4-24.7}), and radiation therapy (OR = 24.1 {4.9-118.4}). When cancer patients (n = 143) were excluded, bisphosphonate use (OR = 7.2 {2.1-24.7}), suppuration (OR = 11.9 {2.0-69.5}), and extractions (OR = 6.6 {1.6-26.6}) remained associated with ONJ. Higher risk of ONJ began within 2 years of bisphosphonate initiation and increased four-fold after 2 years. Both IV and oral bisphosphonate use were strongly associated with ONJ. Duration of treatment > 2 years; suppuration and dental extractions were independent risk factors for ONJ.

Robot-Assisted Needle Steering.

Needle insertion is a critical aspect of many medical treatments, diagnostic methods, and scientific studies, and is considered to be one of the simplest and most minimally invasive medical procedures. Robot-assisted needle steering has the potential to improve the effectiveness of existing medical procedures and enable new ones by allowing increased accuracy through more dexterous control of the needle tip path and acquisition of targets not accessible by straight-line trajectories. In this article, we describe a robot-assisted needle steering system that uses three integrated controllers: a motion planner concerned with guiding the needle around obstacles to a target in a desired plane, a planar controller that maintains the needle in the desired plane, and a torsion compensator that controls the needle tip orientation about the axis of the needle shaft. Experimental results from steering an asymmetric-tip needle in artificial tissue demonstrate the effectiveness of the system and its sensitivity to various environmental and control parameters. In addition, we show an example of needle steering in ex vivo biological tissue to accomplish a clinically relevant task, and highlight challenges of practical needle steering implementation.