Dataset Information

MagnetoSuture: Tetherless Manipulation of Suture Needles.

ABSTRACT: This paper demonstrates the feasibility of ligation and tissue penetration for surgical suturing tasks using magnetically actuated suture needles. Manipulation of suture needles in minimally invasive surgery involves using articulated manual/robotic tools for needle steering and controlling needle-tissue or thread-tissue interactions. The large footprints of conventional articulated surgical tools significantly increase surgical invasiveness, potentially leading to longer recovery times, tissue damage, scarring, or associated infections. Aiming to address these issues, we investigate the feasibility of using magnetic fields to tetherlessly steer suture needles. The primary challenge of such a concept is to provide sufficient force for tissue penetration and ligation. In this work, we demonstrate proof-of-concept capabilities using the MagnetoSuture™ system, performing tissue penetration and ligation tasks using ex vivo tissues, customized NdFeB suture needles with attached threads, and remote-controlled magnetic fields. To evaluate the system performance, we conducted experiments demonstrating tetherless recreation of a purse string suture pattern, ligation of an excised segment of a rat intestine, and penetration of rat intestines.

SUBMITTER: Mair LO

PROVIDER: S-EPMC8570553 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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MagnetoSuture: Tetherless Manipulation of Suture Needles.

Mair Lamar O LO Liu Xiaolong X Dandamudi Bhargav B Jain Kamakshi K Chowdhury Sagar S Weed Jeremy J Diaz-Mercado Yancy Y Weinberg Irving N IN Krieger Axel A

IEEE transactions on medical robotics and bionics 20200417 2

This paper demonstrates the feasibility of ligation and tissue penetration for surgical suturing tasks using magnetically actuated suture needles. Manipulation of suture needles in minimally invasive surgery involves using articulated manual/robotic tools for needle steering and controlling needle-tissue or thread-tissue interactions. The large footprints of conventional articulated surgical tools significantly increase surgical invasiveness, potentially leading to longer recovery times, tissue ...[more]

PMID: 34746679

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Project description:SignificanceSeveral miniaturized optical neuroimaging devices for preclinical studies mimicking benchtop instrumentation have been proposed in the past. However, they are generally relatively large, complex, and power-hungry, limiting their usability for long-term measurements in freely moving animals. Further, there is limited research in the development of algorithms to analyze long-term signals.AimWe aim to develop a cost-effective, easy-to-use miniaturized intrinsic optical monitoring system (TinyIOMS) that can be reliably used to record spontaneous and stimulus-evoked hemodynamic changes and further cluster brain states based on hemodynamic features.ApproachWe present the design and fabrication of TinyIOMS ( 8 mm×13 mm×9 mm3 , 1.2 g with battery). A standard camera-based widefield system (WFIOS) is used to validate the TinyIOMS signals. Next, TinyIOMS is used to continuously record stimulus-evoked activity and spontaneous activity for 7 h in chronically implanted mice. We further show up to 2 days of intermittent recording from an animal. An unsupervised machine learning algorithm is used to analyze the TinyIOMS signals.ResultsWe observed that the TinyIOMS data is comparable to the WFIOS data. Stimulus-evoked activity recorded using the TinyIOMS was distinguishable based on stimulus magnitude. Using TinyIOMS, we successfully achieved 7 h of continuous recording and up to 2 days of intermittent recording in its home cage placed in the animal housing facility, i.e., outside a controlled lab environment. Using an unsupervised machine learning algorithm ( k -means clustering), we observed the grouping of data into two clusters representing asleep and awake states with an accuracy of ∼91% . The same algorithm was then applied to the 2-day-long dataset, where similar clusters emerged.ConclusionsTinyIOMS can be used for long-term hemodynamic monitoring applications in mice. Results indicate that the device is suitable for measurements in freely moving mice during behavioral studies synchronized with behavioral video monitoring and external stimuli.

Project description:The human cortex encodes information in complex networks that can be anatomically dispersed and variable in their microstructure across individuals. Using simulations with neural network models, we show that contemporary statistical methods for functional brain imaging-including univariate contrast, searchlight multivariate pattern classification, and whole-brain decoding with L1 or L2 regularization-each have critical and complementary blind spots under these conditions. We then introduce the sparse-overlapping-sets (SOS) LASSO-a whole-brain multivariate approach that exploits structured sparsity to find network-distributed information-and show in simulation that it captures the advantages of other approaches while avoiding their limitations. When applied to fMRI data to find neural responses that discriminate visually presented faces from other visual stimuli, each method yields a different result, but existing approaches all support the canonical view that face perception engages localized areas in posterior occipital and temporal regions. In contrast, SOS LASSO uncovers a network spanning all four lobes of the brain. The result cannot reflect spurious selection of out-of-system areas because decoding accuracy remains exceedingly high even when canonical face and place systems are removed from the dataset. When used to discriminate visual scenes from other stimuli, the same approach reveals a localized signal consistent with other methods-illustrating that SOS LASSO can detect both widely distributed and localized representational structure. Thus, structured sparsity can provide an unbiased method for testing claims of functional localization. For faces and possibly other domains, such decoding may reveal representations more widely distributed than previously suspected.SIGNIFICANCE STATEMENT Brain systems represent information as patterns of activation over neural populations connected in networks that can be widely distributed anatomically, variable across individuals, and intermingled with other networks. We show that four widespread statistical approaches to functional brain imaging have critical blind spots in this scenario and use simulations with neural network models to illustrate why. We then introduce a new approach designed specifically to find radically distributed representations in neural networks. In simulation and in fMRI data collected in the well studied domain of face perception, the new approach discovers extensive signal missed by the other methods-suggesting that prior functional imaging work may have significantly underestimated the degree to which neurocognitive representations are distributed and variable across individuals.

Dataset Information

MagnetoSuture: Tetherless Manipulation of Suture Needles.

Publications

MagnetoSuture: Tetherless Manipulation of Suture Needles.

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