A review of bio-inspired needle for percutaneous interventions

Ma, Yichi; Xiao, Xiao; Ren, Hongliang; Meng, Max Q.-H.

doi:10.1016/j.birob.2022.100064

Cited by 7 publications

(2 citation statements)

References 63 publications

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“…Still, the most effective route for the problem-driven approach to lead to a successful outcome is to include biologists into the project team (Snell-Rood, 2016;Graeff et al, 2019). The problem-driven approach has to date delivered few success stories with the most promising ones probably being in the area of medical technology including using biomaterials, such as silk, as scaffolds for tissue regeneration and in surgery (Holland et al, 2019;Song et al, 2020), and developing painless microneedles based on the mouth parts and biting behaviour of mosquitoes (Lenau et al, 2017;Ma et al, 2022). Nonetheless, the most successful methodology remains the solution-driven one to which the above mentioned Velcro belongs.…”

Section: What Is Biomimetics and Biologically Inspired Design?mentioning

confidence: 99%

The biomimetic potential of novel adaptations in subterranean animals

Hesselberg

2023

Front. Ecol. Evol.

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Subterranean habitats can be considered harsh conditions with lack of light, low nutrients levels and constant high humidity. To thrive under such conditions, cave-adapted species have evolved a range of novel morphological, physiological and behavioural adaptations. These adaptations might have significant biomimetic potential. Biomimetics or biologically inspired design is a relatively new interdisciplinary field that aims to harness the processes and mechanisms in nature that have been optimised over millions of years’ evolution to improve our own technology. There are two main approaches to biologically inspired design—the problem-driven approach starting with an engineering problem and searching through biological equivalents and the solution-driven approach, which starts with a biological example or solution followed by the identification of a suitable engineering application. While the former approach is the most popular and is favoured by engineers, the latter remains the most successful and is typically driven by fundamental biological research. However, few biomimetic solutions or concepts have so far been described from subterranean habitats despite the rich potential. In this review paper, I first outline the theory behind biologically inspired design before I review the few biomimetic related studies of cave adapted organisms mainly based on the exceptional lateral line systems in blind cave fish. However, the main body of the review focuses on identifying and discussing subterranean adaptations with a particular strong potential including biomimetics sensors, adhesion in high humidity and wet conditions and biomaterials

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Section: What Is Biomimetics and Biologically Inspired Design?mentioning

confidence: 99%

The biomimetic potential of novel adaptations in subterranean animals

Hesselberg

2023

Front. Ecol. Evol.

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“…A novel data-driven paradigm proposed by Sefati et al [22] including uncalibrated FBG sensors and related deep neural network (DNN) and temporal neural network (TNN) was used for sensing in surgical continuum manipulator. Besides, a general and convenient tactile cues paradigm is required for different novel bio-inspired needle designs [23].…”

mentioning

confidence: 99%

Data-Driven 3-D Tactile Cues With Intermediate Soft Interfaces Toward Training Needle Insertions

Tang,

Yao,

Bai

et al. 2024

IEEE Sensors J.

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During the training of medical operators, forming muscle memory through repetitions is a routine path. Needle insertion is a fundamental skill that all medical staff need to master. However, such training courses often require human resources at a relatively high cost. Therefore, a needle insertion simulator for large-scale deployment and practical training is expected. In this paper, we design a passive, compliant force estimator that is low-cost, easily fabricated, and commonly demanded by needle insertion simulators. A triaxial decoupling force sensor design comprises commercial Force Sensing Resistors (FSR), soft silicon materials, and a 3D-printed connector for force decoupling. The total cost of the sensor and fabrication process is less than 5 USD. To achieve the prediction of a 3D force profile when the corresponding medical tasks are performed, we propose and compare two data-driven estimators, including least square (LS) regression and feedforward neural network (FNN). We demonstrate that FNN models outperformed the LS model regarding devised corresponding evaluation metrics. The predicted accuracy of FNN is above 90%, while the LS has a lower average accuracy of 78.02%. Finally, we test the performance of a pre-trained model on different angle gaps (15°, 30°). The force profiles of the 15°angle gap present relatively large fluctuations compared to reference with average accuracy at 84.36%. The test on the 30°angle gap has an error at 35.28%, which also shows randomness deviations from standard profiles. Therefore, the force information with a 15°or 30°angle gap can warn the trainer that an angle deviation exists from the standard setting.

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