Tactile sensor-based real-time clustering for tissue differentiation

Assessment of Obesity as Risk Factor of Lumbar Disc Surgery: Retrospective Analysis of 598 Cases and Simulated Surgery on 3D-Printed Models

(1) Background: Obesity poses known risks in surgery, including a prolonged operation time and postoperative complications. Given the rising obesity rates and frequent lumbar disc surgeries, understanding these risks is crucial. This study aims to assess the impact of obesity on operation duration and postoperative complications in lumbar disc prolapse surgery. (2) Methods: We retrospectively analyzed 598 patients with monosegmental disc herniation, correlating their body mass index (BMI) as a surrogate parameter for obesity with operation time. Excluding complex cases (multi-segmental herniations or recurrent herniations), complication rates and hospital stays were recorded. Simulated surgeries on 3D-printed models of varying obesity levels examined operation times and instrument suitability. (3) Results: Of these patients, 438 patients had a BMI of <30, and 160 patients had a BMI of ≥30. Complication rates showed no significant differences between groups. Linear regression analysis failed to establish a sole dependency of operation time on BMI, with R2 = 0.039 for the normal-weight group (BMI < 30) and R2 = 0.059 for the obese group (BMI ≥ 30). The simulation operations on the 3D-printed models of varying degrees of obesity showed a significant increase in the simulated operation time with higher levels of obesity. A geometrically inadequate set of surgical instruments was assumed to be a significant factor in the simulated increase in operating time. (4) Conclusions: While various factors influence operation time, obesity alone does not significantly increase it. However, simulated surgeries highlighted the impact of obesity, particularly on instrument limitations. Understanding these complexities is vital for optimizing surgical outcomes in obese patients.

Toward intraoperative tissue classification: exploiting signal feedback from an ultrasonic aspirator for brain tissue differentiation

PURPOSE

During brain tumor surgery, care must be taken to accurately differentiate between tumorous and healthy tissue, as inadvertent resection of functional brain areas can cause severe consequences. Since visual assessment can be difficult during tissue resection, neurosurgeons have to rely on the mechanical perception of tissue, which in itself is inherently challenging. A commonly used instrument for tumor resection is the ultrasonic aspirator, whose system behavior is already dependent on tissue properties. Using data recorded during tissue fragmentation, machine learning-based tissue differentiation is investigated for the first time utilizing ultrasonic aspirators.

METHODS

Artificial tissue model with two different mechanical properties is synthesized to represent healthy and tumorous tissue. 40,000 temporal measurement points of electrical data are recorded in a laboratory environment using a CNC machine. Three different machine learning approaches are applied: a random forest (RF), a fully connected neural network (NN) and a 1D convolutional neural network (CNN). Additionally, different preprocessing steps are investigated.

RESULTS

Fivefold cross-validation is conducted over the data and evaluated with the metrics F1, accuracy, positive predictive value, true positive rate and area under the receiver operating characteristic. Results show a generally good performance with a mean F1 of up to 0.900 ± 0.096 using a NN approach. Temporal information indicates low impact on classification performance, while a low-pass filter preprocessing step leads to superior results.

CONCLUSION

This work demonstrates the first steps to successfully differentiate healthy brain and tumor tissue using an ultrasonic aspirator during tissue fragmentation. Evaluation shows that both neural network-based classifiers outperform the RF. In addition, the effects of temporal dependencies are found to be reduced when adequate data preprocessing is performed. To ensure subsequent implementation in the clinic, handheld ultrasonic aspirator use needs to be investigated in the future as well as the addition of data to reflect tissue diversity during neurosurgical operations.