An improved method for assessing the technical accuracy of optical tracking systems for orthopaedic surgical navigation

Deep Learning-Based Fine-Tuning Approach of Coarse Registration for Ear-Nose-Throat (ENT) Surgical Navigation Systems

Accurate registration between medical images and patient anatomy is crucial for surgical navigation systems in minimally invasive surgeries. This study introduces a novel deep learning-based refinement step to enhance the accuracy of surface registration without disrupting established workflows. The proposed method integrates a machine learning model between conventional coarse registration and ICP fine registration. A deep-learning model was trained using simulated anatomical landmarks with introduced localization errors. The model architecture features global feature-based learning, an iterative prediction structure, and independent processing of rotational and translational components. Validation with silicon-masked head phantoms and CT imaging compared the proposed method to both conventional registration and a recent deep-learning approach. The results demonstrated significant improvements in target registration error (TRE) across different facial regions and depths. The average TRE for the proposed method (1.58 ± 0.52 mm) was significantly lower than that of the conventional (2.37 ± 1.14 mm) and previous deep-learning (2.29 ± 0.95 mm) approaches (p < 0.01). The method showed a consistent performance across various facial regions and enhanced registration accuracy for deeper areas. This advancement could significantly enhance precision and safety in minimally invasive surgical procedures.

Accuracy of robotic surgery for dental implant placement: A systematic review and meta-analysis

OBJECTIVES

To systematically analyze the accuracy of robotic surgery for dental implant placement.

MATERIALS AND METHODS

PubMed, Embase, and Cochrane CENTRAL were searched on October 25, 2023. Model studies or clinical studies reporting the accuracy of robotic surgery for dental implant placement among patients with missing or hopeless teeth were included. Risks of bias in clinical studies were assessed. Meta-analyses were undertaken.

RESULTS

Data from 8 clinical studies reporting on 109 patients and 242 implants and 13 preclinical studies were included. Positional accuracy was measured by comparing the implant plan in presurgery CBCT and the actual implant position in postsurgery CBCT. For clinical studies, the pooled (95% confidence interval) platform deviation, apex deviation, and angular deviation were 0.68 (0.57, 0.79) mm, 0.67 (0.58, 0.75) mm, and 1.69 (1.25, 2.12)°, respectively. There was no statistically significant difference between the accuracy of implants placed in partially or fully edentulous patients. For model studies, the pooled platform deviation, apex deviation, and angular deviation were 0.72 (0.58, 0.86) mm, 0.90 (0.74, 1.06) mm, and 1.46 (1.22, 1.70)°, respectively. No adverse event was reported.

CONCLUSION

Within the limitation of the present systematic review, robotic surgery for dental implant placement showed suitable implant positional accuracy and had no reported obvious harm. Both robotic systems and clinical studies on robotic surgery for dental implant placement should be further developed.

Improved positional accuracy of dental implant placement using a haptic and machine-vision-controlled collaborative surgery robot: A pilot randomized controlled trial

AIM

To compare the implant accuracy, safety and morbidity between robot-assisted and freehand dental implant placement.

MATERIALS AND METHODS

Subjects requiring single-site dental implant placement were recruited. Patients were randomly allocated to freehand implant placement and robot-assisted implant placement. Differences in positional accuracy of the implant, surgical morbidity and complications were assessed. The significance of intergroup differences was tested with an intention-to-treat analysis and a per-protocol (PP) analysis (excluding one patient due to calibration error).

RESULTS

Twenty patients (with a median age of 37, 13 female) were included. One subject assigned to the robotic arm was excluded from the PP analysis because of a large calibration error due to the dislodgement of the index. For robot-assisted and freehand implant placement, with the PP analysis, the median (25th-75th percentile) platform global deviation, apex global deviation and angular deviation were 1.23 (0.9-1.4) mm/1.9 (1.2-2.3) mm (p = .03, the Mann-Whitney U-test), 1.40 (1.1-1.6) mm/2.1 (1.7-3.9) mm (p < .01) and 3.0 (0.9-6.0)°/6.7 (2.2-13.9)° (p = .08), respectively. Both methods showed limited damage to the alveolar ridge and had similar peri- and post-operative morbidity and safety.

CONCLUSIONS

Robot-assisted implant placement enabled greater positional accuracy of the implant compared to freehand placement in this pilot trial. The robotic system should be further developed to simplify surgical procedures and improve accuracy and be validated in properly sized trials assessing the full spectrum of relevant outcomes.

Accuracy and safety of a haptic operated and machine vision controlled collaborative robot for dental implant placement: A translational study

OBJECTIVES

Multiple generations of medical robots have revolutionized surgery. Their application to dental implants is still in its infancy. Co-operating robots (cobots) have great potential to improve the accuracy of implant placement, overcoming the limitations of static and dynamic navigation. This study reports the accuracy of robot-assisted dental implant placement in a preclinical model and further applies the robotic system in a clinical case series.

MATERIALS AND METHODS

In model analyses, the use of a lock-on structure at robot arm-handpiece was tested in resin arch models. In a clinical case series, patients with single missing teeth or edentulous arch were included. Robot-assisted implant placement was performed. Surgery time was recorded. Implant platform deviation, apex deviation, and angular deviation were measured. Factors influencing implant accuracy were analyzed.

RESULTS

The in vitro results showed that with a lock-on structure, the mean (SD) of platform deviation, apex deviation, and angular deviation were 0.37 (0.14) mm, 0.44 (0.17) mm, and 0.75 (0.29)°, respectively. Twenty-one patients (28 implants) were included in the clinical case series, 2 with arches and 19 with single missing teeth. The median surgery time for single missing teeth was 23 (IQ range 20-25) min. The surgery time for the two edentulous arches was 47 and 70 min. The mean (SD) of platform deviation, apex deviation, and angular deviation was 0.54 (0.17) mm, 0.54 (0.11) mm, and 0.79 (0.22)° for single missing teeth and for 0.53 (0.17) mm, 0.58 (0.17) mm, and 0.77 (0.26)° for an edentulous arch. Implants placed in the mandible had significantly larger apex deviation than those in the maxilla.

CONCLUSION

Cobot-assisted dental implant placement showed excellent positional accuracy and safety in both the in vitro study and the clinical case series. More technological development and clinical research are needed to support the introduction of robotic surgery in oral implantology. Trial registered in ChiCTR2100050885.

Evaluation of a calibration rig for stereo laparoscopes

BACKGROUND

Accurate camera and hand-eye calibration are essential to ensure high-quality results in image-guided surgery applications. The process must also be able to be undertaken by a nonexpert user in a surgical setting.

PURPOSE

This work seeks to identify a suitable method for tracked stereo laparoscope calibration within theater.

METHODS

A custom calibration rig, to enable rapid calibration in a surgical setting, was designed. The rig was compared against freehand calibration. Stereo reprojection, stereo reconstruction, tracked stereo reprojection, and tracked stereo reconstruction error metrics were used to evaluate calibration quality.

RESULTS

Use of the calibration rig reduced mean errors: reprojection (1.47 mm [SD 0.13] vs. 3.14 mm [SD 2.11], p-value 1e-8), reconstruction (1.37 px [SD 0.10] vs. 10.10 px [SD 4.54], p-value 6e-7), and tracked reconstruction (1.38 mm [SD 0.10] vs. 12.64 mm [SD 4.34], p-value 1e-6) compared with freehand calibration. The use of a ChArUco pattern yielded slightly lower reprojection errors, while a dot grid produced lower reconstruction errors and was more robust under strong global illumination.

CONCLUSION

The use of the calibration rig results in a statistically significant decrease in calibration error metrics, versus freehand calibration, and represents the preferred approach for use in the operating theater.

Accuracy of intraoperative bone registration and stereotactic boundary reconstruction during total knee arthroplasty surgery

BACKGROUND

The intraoperative registration of the bones play a crucial role in image-based computer-assisted knee arthroplasty to achieve accurate implant placement and to create reliable stereotactic bone boundaries for robot-assisted surgical systems.

METHOD

This study assessed the intraoperative registration accuracy on six intact fresh frozen cadavers.

RESULTS

Rotational errors around the mechanical axis were the largest, with a standard deviation of 1.2° and outliers up to 3.7°. The mean translational errors were lower than 1 mm, with outliers up to 1.5 mm. These errors were amplified to 2 mm for the registration-based reconstruction of the posterior bone surface at the resection levels.

CONCLUSION

Given the cumulative behaviour of surgical errors, registration errors can affect the final implant positioning. Furthermore, inaccuracies in the reconstructed bone boundary directly affect the virtual stereotactic boundaries used in robotic-assisted surgery and can result in an incomplete resection or inadvertent soft tissue damage.

A review of advances in image-guided orthopedic surgery

Orthopedic surgery remains technically demanding due to the complex anatomical structures and cumbersome surgical procedures. The introduction of image-guided orthopedic surgery (IGOS) has significantly decreased the surgical risk and improved the operation results. This review focuses on the application of recent advances in artificial intelligence (AI), deep learning (DL), augmented reality (AR) and robotics in image-guided spine surgery, joint arthroplasty, fracture reduction and bone tumor resection. For the pre-operative stage, key technologies of AI and DL based medical image segmentation, 3D visualization and surgical planning procedures are systematically reviewed. For the intra-operative stage, the development of novel image registration, surgical tool calibration and real-time navigation are reviewed. Furthermore, the combination of the surgical navigation system with AR and robotic technology is also discussed. Finally, the current issues and prospects of the IGOS system are discussed, with the goal of establishing a reference and providing guidance for surgeons, engineers, and researchers involved in the research and development of this area.