Multimodal medical image fusion using convolutional neural network and extreme learning machine

A machine learning based approach to standardizing tooth color and shade recommendations

STATEMENT OF PROBLEM

Achieving the precise reproduction of tooth color is pivotal in esthetic restorations. However, existing visual and instrumental shade matching methods, each with inherent limitations, have often resulted in inconsistent color communication and subsequently suboptimal esthetic results in clinical practice.

PURPOSE

The purpose of this in vitro study was to evaluate a 2-tier color correction strategy to accurately acquire standardized tooth color and to provide shade recommendations for esthetic restorations.

MATERIAL AND METHODS

Photographs of a standard color card (ColorChecker Classic; X-rite) and a commercially available shade guide (VITA 3D-Master; Vita Zahnfabrik) were captured under standard lighting conditions. Machine learning (ML) models for color correction, including polynomial regression (PR), backpropagation neural network (BPNN), and extreme learning machine (ELM), were trained using color values extracted from these standardized photographs. Subsequently, photographs made under clinical lighting conditions and featuring both the standard color card and the shade guide underwent the first color correction using the trained ML models. The secondary color correction was then executed based on the custom color space of VITA 3D-Master, yielding corrected color values for shade recommendations. The prediction accuracy of the ML models and the precision of color correction were evaluated using the root mean square error (RMSE), coefficient of determination (R²), and color difference (α=.05 for all statistical analyses).

RESULTS

Compared with the PR and BPNN models, the ELM model provided more precise and reliable predictions with the lowest RMSE (2.2) and the highest R2 (0.996). After 2 rounds of color correction, the color difference was reduced from 7.6 to 1.0, which was lower than the 50:50% acceptability threshold (1.8) and closer to the 50:50% perceptibility threshold (0.8). Furthermore, the matching results between the secondary values and the ground truth of the shade guides achieved an accuracy of 73.1% for shade recommendations.

CONCLUSIONS

The 2-tier color correction strategy based on the ML models and the color space specified by the VITA 3D-Master system effectively standardized the color of dental photographs and provided a more accurate and stable method of communicating color between dentists and dental laboratory technicians.

Application of Medical Image Navigation Technology in Minimally Invasive Puncture Robot

Microneedle puncture is a standard minimally invasive treatment and surgical method, which is widely used in extracting blood, tissues, and their secretions for pathological examination, needle-puncture-directed drug therapy, local anaesthesia, microwave ablation needle therapy, radiotherapy, and other procedures. The use of robots for microneedle puncture has become a worldwide research hotspot, and medical imaging navigation technology plays an essential role in preoperative robotic puncture path planning, intraoperative assisted puncture, and surgical efficacy detection. This paper introduces medical imaging technology and minimally invasive puncture robots, reviews the current status of research on the application of medical imaging navigation technology in minimally invasive puncture robots, and points out its future development trends and challenges.