Tube thoracostomy (chest tube insertion) is a surgical procedure that treats pneumothorax, a potentially life-threatening condition where air accumulates between the chest wall and the lungs. The literature reports high complication rates for this procedure, including accidental fatality due to poor manual depth control during tool insertion. We hypothesize that an instrumented needle-holder could help operators recognize pleural puncture and improve depth control, and we present a puncture-detection experiment that contributes toward this goal. An operator manually inserted a bevel-tip needle into ex vivo porcine ribs and through the parietal pleura via a sensorized percutaneous device that records position, force, and videos. We use this rich dataset of 63 insertions to thoroughly test four previously published data-driven puncture-detection (DDPD) algorithms against two new real-time algorithms: a custom recursive digital filter with coefficients optimized for our application, and a difference equation that compares standard deviations between adjacent sliding windows. Our algorithms achieve a precision (true positives over total identified punctures) of 23% and 22%, respectively, while the precision of existing DDPD algorithms ranges from 0% to 21%. Despite these performance improvements, our results show the limitations of DDPD algorithms and motivate new methods for detecting pleural membrane punctures in thoracostomy.

Chest trauma or disease progression can lead to tension pneumothorax, a condition where mounting pressurization of the pleural cavity (the space between the chest wall and the lungs) leads rapidly to cardiac arrest. In pre-hospital settings, tension pneumothorax is treated by venting the pleural cavity via a needle introduced through the chest wall. Very high failure rates (up to 94.1%) have been reported for pre-hospital needle decompression, however, and the procedure can result in the accidental puncture of critical thoracic tissues because it is performed blind. Instrumented needles could help operators more reliably identify when the tool has entered the target space. This paper investigates technical approaches to provide such support; we created an experimental system that acquires needle force and position signals, as well as the diffuse backscattered reflectance from white light carried to and collected from the needle's tip via two in-bore optical fibers. Data collection occurred while two experimenters inserted a bevel-tipped percutaneous needle into an ex vivo porcine rib section simulating human chest anatomy. Four data-driven puncture-detection (DDPD) algorithms from the literature, which are appropriate for use with the variable tool velocities produced by manual insertions, were applied to the resulting data set offline. Grid search was performed across key signal-processing parameters, high-pass filters (HPFs) were applied to examine their impact on puncture detection, and a first exploration of multimodal (ensemble) methods was performed. Combining high-pass filters with DDPD methods resulted in a 2.7-fold improvement (from 8.2% to 21.9%) in the maximum overall precision (MOP) produced by force signals. Applying this HPF + DDPD scheme to reflectance data streams yielded a peak MOP of 36.4%, and combining reflectance with force generated the best MOP overall (42.1%); these results represent 4.4-fold and 5.1-fold improvements, respectively, over the best MOP produced by the traditional application of DDPD algorithms to force signals alone. These results strongly support the utility of high-pass filters combined with both reflectance-only and multimodal reflectance-plus-force data-driven puncture-detection schemes for needle decompression applications.

Needle thoracostomy (NT) is a surgical procedure that treats one of the most preventable causes of trauma-related death: dangerous accumulations of air between the chest wall and the lungs. However, needle-tip overshoot of the target space can result in the inadvertent puncture of critical structures like the heart. This type of complication is fatal without urgent surgical care, which is not available in resource-poor environments like space. Since NT is done blind, operators rely on tool sensations to identify when the needle has reached its target. Needle instrumentation could enable puncture notifications to help operators limit tool-tip overshoot, but such a solution requires reliable puncture detection from manual (i.e., variable-velocity) needle insertion data streams. Data-driven puncture-detection (DDPD) algorithms are appropriate for this application, but their performance has historically been unacceptably low for use in safety-critical applications. This work contributes towards the development of an intelligent device for manual NT assistance by proposing two novel DDPD algorithms. Three data sets are collected that provide needle forces and displacements acquired during insertions into ex vivo porcine tissue analogs for the human chest, and factors affecting DDPD algorithm performance are analyzed in these data. Puncture event features are examined for each sensor, and the suitability of both accelerometer measurements and diffuse reflectance measurements are evaluated within the context of NT. Finally, DDPD ensembles are proposed that yield a 5.1-fold improvement in precision as compared to the traditional force-only DDPD approach. These results lay a foundation for improving the urgent delivery of percutaneous procedures in space and other resource-poor settings.

The surgical procedure of needle thoracostomy temporarily corrects accidental over-pressurization of the space between the chest wall and the lungs. However, failure rates of up to 94.1\% have been reported, likely because this procedure is done blind: operators estimate by feel when the needle has reached its target. We believe instrumented needles could help operators discern entry into the target space, but limited success has been achieved using force and/or position to try to discriminate needle puncture events during simulated surgical procedures. We thus augmented our needle insertion system with a novel in-bore double-fiber optical setup. Tissue reflectance measurements as well as 3D force, torque, position, and orientation were recorded while two experimenters repeatedly inserted a bevel-tipped percutaneous needle into ex vivo porcine ribs. We applied model-free puncture detection to various filtered time derivatives of each sensor data stream offline. In the held-out test set of insertions, puncture-detection precision improved substantially using reflectance measurements compared to needle insertion force alone (3.3-fold increase) or position alone (11.6-fold increase).

Astronauts are at risk for pneumothorax, a condition where injury or disease introduces air between the chest wall and the lungs (i.e., the pleural cavity). In a worst-case scenario, it can rapidly lead to a fatality if left unmanaged and will require prompt treatment in situ if developed during spaceflight. Chest tube insertion is the definitive treatment for pneumothorax, but it requires a high level of skill and frequent practice for safe use. Physician astronauts may struggle to maintain this skill on medium- and long-duration exploration-class missions, and it is inappropriate for pure just-in-time learning or skill refreshment paradigms. This paper proposes semi-automating tool insertion to reduce the risk of complications in austere environments and describes preliminary experiments providing initial validation of an intelligent prototype system. Specifically, we showcase and analyse motion and force recordings from a sensorized percutaneous access needle inserted repeatedly into an ex vivo tissue phantom, along with relevant physiological data simultaneously recorded from the operator. When coupled with minimal just-in-time training and/or augmented reality guidance, the proposed system may enable non-expert operators to safely perform emergency chest tube insertion without the use of ground resources.

Astronauts are at risk for pneumothorax, a medical condition where air accumulating between the chest wall and the lungs impedes breathing and can result in fatality. Treatments include needle decompression (ND) and chest tube insertion (tube thoracostomy, TT). Unfortunately, the literature reports very high failure rates for ND and high complication rates for TT– especially whenn performed urgently, infrequently, or by inexperienced operators. These statistics are problematic in the context of skill retention for physician astronauts on long-duration exploration-class missions, or for non-medical astronauts if the physician astronaut is the one in need of treatment. We propose reducing the medical risk for exploration-class missions by improving ND/TT outcomes using a robot-based paradigm that automates tool depth control. Our goal is to produce a robotic system that improves the safety of pneumothorax treatments regardless of operator skill and without the use of ground resources. This poster provides an overview of our team's work toward this goal, including robot instrumentation schemes, tool-tissue interaction characterization, and automated puncture detection.

Pneumothorax, a condition where injury or disease introduces air between the chest wall and lungs, can impede lung function and lead to respiratory failure and/or obstructive shock. Chest trauma from dynamic loads, hypobaric exposure from extravehicular activity, and pulmonary inflammation from celestial dust exposures could potentially cause pneumothoraces during spaceflight with or without exacerbation from deconditioning. On Earth, emergent cases are treated with chest tube insertion (tube thoracostomy, TT) when available, or needle decompression (ND) when not (i.e., pre-hospital). However, ND has high failure rates (up to 94\%), and TT has high complication rates (up to 37.9\%), especially when performed by inexperienced or intermittent operators. Thus neither procedure is ideal for a pure just-in-time training or skill refreshment approach, and both may require adjuncts for safe inclusion in Level of Care IV (e.g., short duration lunar orbit) or V (e.g., Mars transit) missions. Insertional complications are of particular concern since they cause inadvertent tissue damage that, while surgically repairable in an operating room, could result in (preventable) fatality in a spacecraft or other isolated, confined, or extreme (ICE) environments. Tools must be positioned and oriented correctly to avoid accidental insertion into critical structures, and they must be inserted no further than the thin membrane lining the inside of the rib cage (i.e., the parietal pleura). Operators identify pleural puncture via loss-of-resistance sensations on the tool during advancement, but experienced surgeons anecdotally describe a wide range of membrane characteristics: robust tissues require significant force to perforate, while fragile tissues deliver little-to-no haptic sensation when pierced. Both extremes can lead to tool overshoot and may be representative of astronaut tissues at the beginning (healthy) and end (deconditioned) of long duration exploration class missions. Given uncertainty surrounding physician astronaut selection criteria, skill retention, and tissue condition, an adjunct for improved insertion accuracy would be of value. We describe experiments conducted with an intelligent prototype sensorized system aimed at semi-automating tool insertion into the pleural cavity. The assembly would integrate with an in-mission medical system and could be tailored to fully complement an autonomous medical response agent. When coupled with minimal just-in-time training, it has the potential to bestow expert pleural access skills on non-expert operators without the use of ground resources, in both emergent and elective treatment scenarios.

Serious complications during chest tube insertion are relatively rare, but can have catastrophic repercussions. We propose semi-automating tool insertion to help protect against non-target tissue puncture, and report first steps collecting and characterizing needle-tissue interaction forces in a tissue phantom used for chest tube insertion training.