Accuracy of Computer-Aided Techniques in Orthopaedic Surgery: How Can It Be Defined, Measured Experimentally, and Analyzed from a Clinical Perspective?

Recent Advances in Digital Technology in Implant Dentistry

Digital technology has emerged as a transformative tool in dental implantation, profoundly enhancing accuracy and effectiveness across multiple facets, such as diagnosis, preoperative treatment planning, surgical procedures, and restoration delivery. The multiple integration of radiographic data and intraoral data, sometimes with facial scan data or electronic facebow through virtual planning software, enables comprehensive 3-dimensional visualization of the hard and soft tissue and the position of future restoration, resulting in heightened diagnostic precision. In virtual surgery design, the incorporation of both prosthetic arrangement and individual anatomical details enables the virtual execution of critical procedures (e.g., implant placement, extended applications, etc.) through analysis of cross-sectional images and the reconstruction of 3-dimensional surface models. After verification, the utilization of digital technology including templates, navigation, combined techniques, and implant robots achieved seamless transfer of the virtual treatment plan to the actual surgical sites, ultimately leading to enhanced surgical outcomes with highly improved accuracy. In restoration delivery, digital techniques for impression, shade matching, and prosthesis fabrication have advanced, enabling seamless digital data conversion and efficient communication among clinicians and technicians. Compared with clinical medicine, artificial intelligence (AI) technology in dental implantology primarily focuses on diagnosis and prediction. AI-supported preoperative planning and surgery remain in developmental phases, impeded by the complexity of clinical cases and ethical considerations, thereby constraining widespread adoption.

Learning curve of dynamic navigation-assisted zygomatic implant surgery: An in vitro study

STATEMENT OF PROBLEM

Dynamic computer-assisted zygomatic implant surgery (dCAZIS) has been reported to provide clinical efficacy with high accuracy and low risk of complications. However, the learning curve before performing dCAZIS effectively is unknown.

PURPOSE

The purpose of this in vitro study was to explore the learning curve of dCAZIS in dentists with different levels of experience in implant dentistry and navigation surgery.

MATERIAL AND METHODS

Six senior dental students were randomly divided into 3 groups for initial training (FH-CI group: pretraining on freehand conventional implant surgery; FH-ZI group: pretraining on freehand ZI surgery; DN-CI group: pretraining on conventional implant surgery under dynamic navigation). Then, every operator conducted 6 repeated dCAZIS training sessions on edentulous 3-dimensional (3D) printed skull models and was asked to complete a self-report questionnaire after each training session. A total of 36 postoperative cone beam computed tomography (CBCT) scans with 144 ZI osteotomy site preparations were obtained and superimposed over the preoperative design for accuracy measurements. The operation time, 3D deviations, and results of the self-reports were recorded. Comparisons among groups were analyzed with independent-sample Kruskal-Wallis tests (α=.05), and correlations between study outcomes and the number of practices were calculated.

RESULTS

Operator experience and increased practice times did not significantly affect the accuracy of dCAZIS (P>.05). However, the operation time varied among groups (P<.001), and significantly shortened with more practice, reaching 11.51 ±1.68 minutes at the fifth attempt in the FH-CI group (P<.001 compared with the first practice), 14.48 ±3.07 minutes at the third attempt in the FH-ZI group (P=.038), and 8.68 ±0.58 minutes at the sixth attempt in the DN-CI group (P<.001). All groups reached their own learning curve plateau stage within 6 practice sessions. As the number of practice sessions increased, the results from the self-report questionnaires gradually improved.

CONCLUSIONS

Among dentists with different levels of experience in implant dentistry and navigation surgery, dCAZIS was found to have a learning curve with respect to operation time but not implant accuracy. Experience in ZI surgery had little impact on the learning curve of dCAZIS, but experience in navigation surgery was a key factor.

Clinical efficacy of computer-assisted zygomatic implant surgery: A systematic scoping review

STATEMENT OF PROBLEM

Digital technology can improve the success of zygomatic implant (ZI) surgery. However, the reliability and efficacy of computer-assisted zygomatic implant surgery (CAZIS) need further analysis.

PURPOSE

The purpose of this scoping review was to provide an overview of the placement accuracy, implant survival, and complications of CAZIS.

MATERIAL AND METHODS

A systematic search of English and Mandarin Chinese publications up to May 2023 was conducted in PubMed, Web of Science, Embase, and Wanfang database. The nonpeer-reviewed literature was searched in the trial register (clinicaltrials.gov). Clinical studies and cadaver studies on CAZIS were included. After data extraction and collection, the findings were critically reviewed, analyzed, interpreted, and discussed.

RESULTS

Forty-one studies met the inclusion criteria. After excluding publications with duplicate data, retaining the most recent, 28 articles were included in this scoping review. Of these, 18 were on static computer-assisted zygomatic implant surgery (sCAZIS), 8 on dynamic computer-assisted zygomatic implant surgery (dCAZIS), and 2 on robot-assisted zygomatic implant surgery (rAZIS). Excluding the outliers, the mean deviations of ZIs in the sCAZIS group (with 8 articles reporting implant placement accuracy, 183 ZIs involved) were: 1.15 ±1.37 mm (coronal deviation), 2.29 ±1.95 mm (apical deviation), and 3.32 ±3.36 degrees (angular deviation). The mean deviations of dCAZIS (3 articles, 251 ZIs) were: 1.60 ±0.74 mm (coronal), 2.27 ±1.05 mm (apical), and 2.89 ±1.69 degrees (angular). The mean deviations of rAZIS (2 articles, 5 ZIs) were: 0.82 ±0.21 mm (coronal), 1.25 ±0.52 mm (apical), and 1.46 ±0.35 degrees (angular). Among the CAZIS reported in the literature, the implant survival rate was high (96.3% for sCAZIS, 98.2% for dCAZIS, and 100% for rAZIS, specified in 14 of 21 clinical studies). The incidence of complications was low, but, because of the few relevant studies (4/21 specified), valid conclusions regarding complications could not be drawn.

CONCLUSIONS

CAZIS has demonstrated clinical efficacy with high implant survival rates and placement accuracy. Of the 3 guided approaches, rAZIS showed the smallest 3-dimensional deviation.

Application of respiratory sensing technique in CT-guided radiofrequency ablation of liver malignancies

Objective

This study aimed to examine the effectiveness and safety of respiratory sensing methods in nonvascular interventional therapy of liver tumors.

Materials and Methods

In this study, 64 patients with primary liver cancer or liver metastasis were retrospectively analyzed. According to two widely used clinical techniques to limit respiratory movement-breath holding and respiratory sensing technology-they were randomly allocated into two groups: respiratory gated and respiratory training. We aimed to compare the application and effect of these two techniques in the nonvascular interventional therapy of liver tumors.

Results

The puncture times of the respiratory-gated and respiratory training groups were 5.34 ± 2.47 and 8.41 ± 3.63 min, respectively. Puncture errors were 10.00 ± 2.65 and 12.81 ± 8.57 mm, respectively. Puncture adjustment times were 3.06 ± 1.26 and 4.87 ± 1.69 times, respectively, and the differences were statistically significant (P < 0.01).

Conclusions

Respiratory sensing technology has been effectively used to assist in puncturing liver malignant tumors using a radiofrequency (RF) ablation (RFA) system that is guided by computed tomography (CT) scans. It is superior to the classical breath-holding step puncture technique in terms of puncture time, puncture error, and puncture needle adjustment times.

The accuracy of navigated versus freehand curettage in bone tumors: a cadaveric model study

PURPOSE

Navigation has been suggested to guide complex benign bone tumor curettage procedures, but the contribution of navigation to the accuracy of curettage has never been quantified. We explored the accuracy of navigated curettage in a cadaveric observational pilot study, comparing navigated to freehand curettage, performed independently by an expert and a novice user.

METHODS

The expert performed curettage on 20 cadaveric bones prepared with a paraffin wax mixture tumor, 10 freehand and 10 navigated. We re-used 12 bones for the novice experiments, 6 freehand and 6 navigated. Tumor and curettage cavity volumes were segmented on pre- and post-cone-beam CT scans. Accuracy was quantified using the Dice Similarity Coefficient (DSC), and with remaining tumor volume, bone curettage volume, maximal remaining width and procedure times compared between navigation and freehand groups for both users.

RESULTS

There were little differences in curettage accuracy between a navigated (DSC 0.59[0.17]) and freehand (DSC 0.64[0.10]) approach for an expert user, but there were for a novice user with DSC 0.67(0.14) and 0.83(0.06), respectively. All navigated and freehand procedures had some amount of remaining tumor, generally located in a few isolated spots with means of 2.2(2.6) cm³ (mean 20% of the tumor volume) and 1.5(1.4) cm³ (18%), respectively, for the expert and more diffusely spaced with means of 5.1(2.8) cm³ (33%) and 3.0(2.2) cm³ (17%), respectively, for the novice.

CONCLUSIONS

In an explorative study on 20 cadaveric bone tumor models, navigated curettage in its current setup was not more accurate than freehand curettage. The amount of remaining tumor, however, confirms that curettage could be further improved. The novice user was less accurate using navigation than freehand, which could be explained by the learning curve. Furthermore, the expert used a different surgical approach than the novice, focusing more on removing the entire tumor than sparing surrounding bone.

Accuracy of Lumbosacral Pedicle Screw Placement in Dogs: A Novel 3D Printed Patient-Specific Drill Guide versus Freehand Technique in Novice and Expert Surgeons

OBJECTIVE

 The aim of this study was to compare the accuracy of pedicle screw placement at the canine lumbosacral junction using a novel unilateral three-dimensional printed patient-specific guide (3D-PSG) versus a freehand drilling technique. Additionally, accuracy of screw placement between a novice and an experienced surgeon was determined.

STUDY DESIGN

 Preoperative computed tomography images from 20 lumbosacral cadaveric specimens were used to design a novel unilateral 3D-PSG for the L7 and sacral vertebrae which was printed in acryl-nitrile butadiene styrene plastic. A novice and an expert surgeon each placed 3.5mm cortical screws in 10 cadavers; on the left using the unilateral 3D-PSG and by the freehand (anatomic landmark) technique on the right.

RESULTS

 Sixty screws were placed using the unilateral 3D-PSG and 60 using the freehand technique. There was no statistical difference in accuracy for the comparison between methods performed by the expert (p = 0.679) and novice (p = 0.761) surgeon, nor between an expert and novice surgeon overall (p = 0.923). Unexpectedly, the use of a unilateral 3D-PSG increased variability for the expert surgeon in our study (p = 0.0314).

CONCLUSION

 Using a novel unilateral 3D-PSG did not improve the accuracy of screw placement for lumbosacral stabilization by a novice surgeon compared with an expert surgeon in lumbar spine surgery. This may reflect a suboptimal PSG design.

Computer assisted orthopaedic surgery: Past, present and future

Computer technology is ubiquitous and relied upon in virtually all professional activities including neurosurgery, which is why it is surprising that it is not the case for orthopaedic surgery with fewer than 5% of surgeons using available computer technology in their procedures. In this review, we explore the evolution and background of Computer Assisted Orthopaedic Surgery (CAOS), delving into the basic principles behind the technology and the changes in the discussion on the subject throughout the years and the impact these discussions had on the field. We found evidence that industry had an important role in driving the discussion at least in knee arthroplasty-a leading field of CAOS-with the ratio between patents and publications increased from approximately 1:10 in 2004 to almost 1:3 in 2014. The adoption of CAOS is largely restrained by economics and ergonomics with sceptics challenging the accuracy and precision of navigation during the early years of CAOS moving to patient functional improvements and long term survivorship. Nevertheless, the future of CAOS remains positive with the prospect of new technologies such as improvements in image-guided surgery, enhanced navigation systems, robotics and artificial intelligence.

Confidence, prediction, and tolerance in linear mixed models

The literature about Prediction Interval (PI) and Tolerance Interval (TI) in linear mixed models is usually developed for specific designs, which is a main limitation to their use. This paper proposes to reformulate the two‐sided PI to be generalizable under a wide variety of designs (one random factor, nested and crossed designs for multiple random factors, and balanced or unbalanced designs). This new methodology is based on the Hessian matrix, namely, the inverse of (observed) Fisher Information matrix, and is built with a cell mean model. The degrees of freedom for the total variance are calculated with the generalized Satterthwaite method and compared to the Kenward‐Roger's degrees of freedom for fixed effects. Construction of two‐sided TIs are also detailed with one random factor, and two nested and two crossed random variables. An extensive simulation study is carried out to compare the widths and coverage probabilities of Confidence Intervals (CI), PIs, and TIs to their nominal levels. It shows excellent coverage whatever the design and the sample size are. Finally, these CIs, PIs, and TIs are applied to two real data sets: one from orthopedic surgery study (intralesional resection risk) and the other from assay validation study during vaccine development.

Novel curved surface preparation technique for knee resurfacing

Conventional tools are incapable of preparing the curved articular surface geometry required during cartilage repair procedures. A novel curved surface preparation technique was proposed and tested to provide an accurate low-cost solution. Three shapes of samples, with flat, 30 mm radius and 60 mm radius surfaces, were manufactured from foam bone substitute for testing. Registering guides and cutting guides were designed and 3-D printed to fit onto the foam samples. A rotational cutting tool with an adapter was used to prepare the surfaces following the guidance slots in the cutting guides. The accuracies of the positions and shapes of the prepared cavities were measured using a digital calliper, and the surface depth accuracy was measured using a 3-D scanner. The mean shape and position errors were both approximately ± 0.5 mm and the mean surface depth error ranged from 0 to 0.3 mm, range - 0.3 to + 0.45 mm 95% CI. This study showed that the technique was able to prepare a curved surface accurately; with some modification it can be used to prepare the knee surface for cartilage repair.

Computer-aided surgery meets predictive, preventive, and personalized medicine

Computer-aided surgery (CAS) is now nearly 30 years old. It has brought to surgery a variety of tools, techniques, and paradigm changes that have had an impact on how surgeries are planned, executed, and evaluated. In this review, we examine the predictive, preventive, and personalized medicine (PPPM) aspects of CAS. We present a brief history of CAS, summarize its the state of the art, and discuss current trends and future developments related to PPPM. Of the three Ps, we note that the most important impact of CAS is on Personalization, in all the steps of the surgical treatment: preoperative planning, intraoperative execution, and postoperative evaluation. Prediction in CAS is reflected in the preoperative evaluation of the various surgical options and in the evaluation of the possible surgical outcomes. Prevention in CAS is related to intraoperative execution, to help prevent possible surgical complications. We foresee that CAS will play an increasingly important role in PPPM in the coming years.