The magnitude of forces and moments applied on teeth during orthodontic treatment is crucial to achieve the desired tooth movement. The aim of this study is to introduce a modular 3D printable orthodontic measurement apparatus (M3DOMA), which can be used for measurements of forces and moments acting on teeth during treatment with aligners. The measurement device was characterized regarding signal to noise ratio (SNR) of the sensors, repeatability of measurements, influence of thermoforming, as well as reliability. Forces and moments were evaluated for an activation range of 0.1–0.4 mm, comparing them among different activation patterns with two aligner thicknesses. The sensors exhibited a SNR from 13–33 dB. Repeatability with repeated measurements showed standard deviations ≤0.015 N and 0.769 Nmm. The influence of thermoforming represented by standard deviation of forces ranges from 0.019–0.147 N. The device showed a range of intra class correlation (ICC) for repeated measurements for all sensors from 0.932 to 0.999. Hence the reliability of the device has been proven to be excellent.

<p>In this study, for the simultaneous measurement of axial forces, temperature, and bending curvature, we analyzed Fiber Bragg Grating (FBG) and Fiber Bragg Grating Fabry- Perot Interferometer (FBGFPI) sensors embedded in 170 μm thin optical fibers, which are suitable for medical applications such as implant strain measurements, catheter ablation, endovascular guidewires, and temperature sensing during thermal therapies. The evaluation of the sensor signals was conducted using both traditional machine learning methods and conventional methods. Multiple datasets were generated for the training of the models, each containing labeled sensor signals with a predefined parameter range of pressure, bending, and temperature. Our findings indicate that we were able to simultaneously detect axial tip pressure with a root-mean-square error (RMSE) of 0.053 bar (R2 = 0.99), local temperature with an RMSE of 0.026 °C (R2 = 1.00), and bending with an RMSE of 0.0019 1/cm (R2 = 1.00). We found that machine learning consistently outperformed conventional approaches. Moreover, for simultaneous measurement of bending, temperature, and pressure, small (< 5mm) Fiber Bragg Gratings without additional mechanical or structural augmentation were sufficient, paving the way for innovative biomedical devices incorporating fiber optic sensing technology. </p>

<p>In this study, for the simultaneous measurement of axial forces, temperature, and bending curvature, we analyzed Fiber Bragg Grating (FBG) and Fiber Bragg Grating Fabry- Perot Interferometer (FBGFPI) sensors embedded in 170 μm thin optical fibers, which are suitable for medical applications such as implant strain measurements, catheter ablation, endovascular guidewires, and temperature sensing during thermal therapies. The evaluation of the sensor signals was conducted using both traditional machine learning methods and conventional methods. Multiple datasets were generated for the training of the models, each containing labeled sensor signals with a predefined parameter range of pressure, bending, and temperature. Our findings indicate that we were able to simultaneously detect axial tip pressure with a root-mean-square error (RMSE) of 0.053 bar (R2 = 0.99), local temperature with an RMSE of 0.026 °C (R2 = 1.00), and bending with an RMSE of 0.0019 1/cm (R2 = 1.00). We found that machine learning consistently outperformed conventional approaches. Moreover, for simultaneous measurement of bending, temperature, and pressure, small (< 5mm) Fiber Bragg Gratings without additional mechanical or structural augmentation were sufficient, paving the way for innovative biomedical devices incorporating fiber optic sensing technology. </p>