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

Virtual Surgical Planning and Patient-Specific Instruments for Correcting Lower Limb Deformities in Pediatric Patients: Preliminary Results from the In-Office 3D Printing Point of Care

(1) Background: Virtual reality and 3D printing are transforming orthopedic surgery by enabling personalized three-dimensional (3D) models for surgical planning and Patient-Specific Instruments (PSIs). Hospitals are establishing in-house 3D printing centers to reduce costs and improve patient care. Pediatric orthopedic surgery also benefits from these technologies, enhancing the precision and personalization of treatments. This study presents preliminary results of an In-Office 3D Printing Point of Care (PoC), outlining considerations and challenges in using this program for treating lower limb deformities in pediatric patients through Virtual Surgical Planning (VSP) and 3D-printed Patient-Specific Instruments (PSIs). (2) Materials and Methods: Pediatric patients with congenital or acquired lower limb deformities undergoing surgical correction based on VSP, incorporating 3D-printed PSIs when required, were included in this study. The entire process of VSP and 3D printing at the In-Office PoC was illustrated. Data about deformity characteristics, surgical procedures, and outcomes, including the accuracy of angular correction, surgical times, and complications, were reported. (3) Results: In total, 39 bone correction procedures in 29 patients with a mean age of 11.6 ± 4.7 years (range 3.1-18.5 years) were performed according to VSP. Among them, 23 procedures were accomplished with PSIs. Surgeries with PSIs were 45 min shorter, with fewer fluoroscopy shots. Optimal correction was achieved in 37% of procedures, while the remaining cases showed under-corrections (41%) or over-corrections (22%). Major complications were observed in four patients (13.8%). (4) Conclusions: The In-Office 3D Printing Point of Care is becoming an essential tool for planning and executing complex corrections of lower limb deformities, but additional research is needed for optimizing the prediction and accuracy of the achieved corrections.

Synergy between artificial intelligence and precision medicine for computer-assisted oral and maxillofacial surgical planning

OBJECTIVES

The aim of this review was to investigate the application of artificial intelligence (AI) in maxillofacial computer-assisted surgical planning (CASP) workflows with the discussion of limitations and possible future directions.

MATERIALS AND METHODS

An in-depth search of the literature was undertaken to review articles concerned with the application of AI for segmentation, multimodal image registration, virtual surgical planning (VSP), and three-dimensional (3D) printing steps of the maxillofacial CASP workflows.

RESULTS

The existing AI models were trained to address individual steps of CASP, and no single intelligent workflow was found encompassing all steps of the planning process. Segmentation of dentomaxillofacial tissue from computed tomography (CT)/cone-beam CT imaging was the most commonly explored area which could be applicable in a clinical setting. Nevertheless, a lack of generalizability was the main issue, as the majority of models were trained with the data derived from a single device and imaging protocol which might not offer similar performance when considering other devices. In relation to registration, VSP and 3D printing, the presence of inadequate heterogeneous data limits the automatization of these tasks.

CONCLUSION

The synergy between AI and CASP workflows has the potential to improve the planning precision and efficacy. However, there is a need for future studies with big data before the emergent technology finds application in a real clinical setting.

CLINICAL RELEVANCE

The implementation of AI models in maxillofacial CASP workflows could minimize a surgeon's workload and increase efficiency and consistency of the planning process, meanwhile enhancing the patient-specific predictability.

Salivary microbiome and metabolome analysis of severe early childhood caries

BACKGROUND

Severe early childhood caries (SECC) is an inflammatory disease with complex pathology. Although changes in the oral microbiota and metabolic profile of patients with SECC have been identified, the salivary metabolites and the relationship between oral bacteria and biochemical metabolism remains unclear. We aimed to analyse alterations in the salivary microbiome and metabolome of children with SECC as well as their correlations. Accordingly, we aimed to explore potential salivary biomarkers in order to gain further insight into the pathophysiology of dental caries.

METHODS

We collected 120 saliva samples from 30 children with SECC and 30 children without caries. The microbial community was identified through 16S ribosomal RNA (rRNA) gene high-throughput sequencing. Additionally, we conducted non-targeted metabolomic analysis through ultra-high-performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry to determine the relative metabolite levels and their correlation with the clinical caries status.

RESULTS

There was a significant between-group difference in 8 phyla and 32 genera in the microbiome. Further, metabolomic and enrichment analyses revealed significantly altered 32 salivary metabolites in children with dental caries, which involved pathways such as amino acid metabolism, pyrimidine metabolism, purine metabolism, ATP-binding cassette transporters, and cyclic adenosine monophosphate signalling pathway. Moreover, four in vivo differential metabolites (2-benzylmalate, epinephrine, 2-formaminobenzoylacetate, and 3-Indoleacrylic acid) might be jointly applied as biomarkers (area under the curve = 0.734). Furthermore, the caries status was correlated with microorganisms and metabolites. Additionally, Spearman's correlation analysis of differential microorganisms and metabolites revealed that Veillonella, Staphylococcus, Neisseria, and Porphyromonas were closely associated with differential metabolites.

CONCLUSION

This study identified different microbial communities and metabolic profiles in saliva, which may be closely related to caries status. Our findings could inform future strategies for personalized caries prevention, detection, and treatment.

Realizing the Potential of Computer-Assisted Surgery by Embedding Digital Twin Technology

The value of virtual world and digital phenotyping has been demonstrated in several fields, and their applications in the field of surgery are worthy of attention and exploration. This viewpoint describes the necessity and approach to understanding the deeper potential of computer-assisted surgery through interaction and symbiosis between virtual and real spaces. We propose to embed digital twin technology into all aspects of computer-assisted surgery rather than just the surgical object and further apply it to the whole process from patient treatment to recovery. A more personalized, precise, and predictable surgery is our vision.

Application of trans-sutural distraction osteogenesis based on an optical surgical navigation system to correct midfacial dysplasia

Midfacial hypoplasia is a common maxillofacial deformity in patients with cleft lip and palate, which requires surgical treatment. However, trans-sutural distraction osteogenesis (TSDO) remains some disadvantages, including difficulty in accurate location of surgical path, prolonged operation time, and excess surgical bleeding. This study aimed to evaluate the application of an optical surgical navigation system (OSNS) developed for TSDO. Six consecutive patients with midfacial hypoplasia who required TSDO were included in the study. Preoperatively, a head computed tomography was performed, and the data were imported into Mimics software (version: 20.0.0.691, Materialise Inc, Belgium) to design a three-dimensional simulation of the surgical approach. TSDO was performed with the use of OSNS. The accuracy and results of the procedure were initially evaluated by comparing the preoperative and postoperative periods. The patients included five boys and one girl, with an average age of 10 years; five with postoperative cleft lip and palate, and one without combined cleft lip and palate. The surgical procedure was successful, with a postoperative follow-up of 4–5 months. All patients demonstrated good treatment results without complications. In conclusioin, OSNS-assisted TSDO can noninvasively correct midfacial dysplasia, improve surgical precision, reduce bleeding and obtain better clinical results. OSNS can guide the TSDO safely and effectively.

Surgical Process Modeling for Open Spinal Surgeries

Modern operating rooms are becoming increasingly advanced thanks to the emerging medical technologies and cutting-edge surgical techniques. Current surgeries are transitioning into complex processes that involve information and actions from multiple resources. When designing context-aware medical technologies for a given intervention, it is of utmost importance to have a deep understanding of the underlying surgical process. This is essential to develop technologies that can correctly address the clinical needs and can adapt to the existing workflow. Surgical Process Modeling (SPM) is a relatively recent discipline that focuses on achieving a profound understanding of the surgical workflow and providing a model that explains the elements of a given surgery as well as their sequence and hierarchy, both in quantitative and qualitative manner. To date, a significant body of work has been dedicated to the development of comprehensive SPMs for minimally invasive baroscopic and endoscopic surgeries, while such models are missing for open spinal surgeries. In this paper, we provide SPMs common open spinal interventions in orthopedics. Direct video observations of surgeries conducted in our institution were used to derive temporal and transitional information about the surgical activities. This information was later used to develop detailed SPMs that modeled different primary surgical steps and highlighted the frequency of transitions between the surgical activities made within each step. Given the recent emersion of advanced techniques that are tailored to open spinal surgeries (e.g., artificial intelligence methods for intraoperative guidance and navigation), we believe that the SPMs provided in this study can serve as the basis for further advancement of next-generation algorithms dedicated to open spinal interventions that require a profound understanding of the surgical workflow (e.g., automatic surgical activity recognition and surgical skill evaluation). Furthermore, the models provided in this study can potentially benefit the clinical community through standardization of the surgery, which is essential for surgical training.

Predictive factors, preventive implications, and personalized surgical strategies for bone metastasis from lung cancer: population-based approach with a comprehensive cancer center-based study

Background

Bone metastasis (BM) and skeletal-related events (SREs) happen to advanced lung cancer (LC) patients without warning. LC-BM patients are often passive to BM diagnosis and surgical treatment. It is necessary to guide the diagnosis and treatment paradigm for LC-BM patients from reactive medicine toward predictive, preventive, and personalized medicine (PPPM) step by step.

Methods

Two independent study cohorts including LC-BM patients were analyzed, including the Surveillance, Epidemiology, and End Results (SEER) cohort (n = 203942) and the prospective Fudan University Shanghai Cancer Center (FUSCC) cohort (n = 59). The epidemiological trends of BM in LC patients were depicted. Risk factors for BM were identified using a multivariable logistic regression model. An individualized nomogram was developed for BM risk stratification. Personalized surgical strategies and perioperative care were described for FUSCC cohort.

Results

The BM incidence rate in LC patients grew (from 17.53% in 2010 to 19.05% in 2016). Liver metastasis was a significant risk factor for BM (OR = 4.53, 95% CI = 4.38-4.69) and poor prognosis (HR = 1.29, 95% CI = 1.25-1.32). The individualized nomogram exhibited good predictive performance for BM risk stratification (AUC = 0.784, 95%CI = 0.781-0.786). Younger patients, males, patients with high invasive LC, and patients with other distant site metastases should be prioritized for BM prevention. Spine is the most common site of BM, causing back pain (91.5%), pathological vertebral fracture (27.1%), and difficult walking (25.4%). Spinal surgery with personalized spinal reconstruction significantly relieved pain and improved daily activities. Perioperative inflammation, immune, and nutrition abnormities warrant personalized managements. Radiotherapy needs to be recommended for specific postoperative individuals.

Conclusions

The presence of liver metastasis is a strong predictor of LC-BM. It is recommended to take proactive measures to prevent BM and its SREs, particularly in young patients, males, high invasive LC, and LC with liver metastasis. BM surgery and perioperative management are personalized and required. In addition, adjuvant radiation following separation surgery must also be included in PPPM-guided management.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00270-9.

SpineDepth: A Multi-Modal Data Collection Approach for Automatic Labelling and Intraoperative Spinal Shape Reconstruction Based on RGB-D Data

Computer aided orthopedic surgery suffers from low clinical adoption, despite increased accuracy and patient safety. This can partly be attributed to cumbersome and often radiation intensive registration methods. Emerging RGB-D sensors combined with artificial intelligence data-driven methods have the potential to streamline these procedures. However, developing such methods requires vast amount of data. To this end, a multi-modal approach that enables acquisition of large clinical data, tailored to pedicle screw placement, using RGB-D sensors and a co-calibrated high-end optical tracking system was developed. The resulting dataset comprises RGB-D recordings of pedicle screw placement along with individually tracked ground truth poses and shapes of spine levels L1-L5 from ten cadaveric specimens. Besides a detailed description of our setup, quantitative and qualitative outcome measures are provided. We found a mean target registration error of 1.5 mm. The median deviation between measured and ground truth bone surface was 2.4 mm. In addition, a surgeon rated the overall alignment based on 10% random samples as 5.8 on a scale from 1 to 6. Generation of labeled RGB-D data for orthopedic interventions with satisfactory accuracy is feasible, and its publication shall promote future development of data-driven artificial intelligence methods for fast and reliable intraoperative registration.

The Modern and Digital Transformation of Oral Health Care: A Mini Review

Dentistry is a part of the field of medicine which is advocated in this digital revolution. The increasing trend in dentistry digitalization has led to the advancement in computer-derived data processing and manufacturing. This progress has been exponentially supported by the Internet of medical things (IoMT), big data and analytical algorithm, internet and communication technologies (ICT) including digital social media, augmented and virtual reality (AR and VR), and artificial intelligence (AI). The interplay between these sophisticated digital aspects has dramatically changed the healthcare and biomedical sectors, especially for dentistry. This myriad of applications of technologies will not only be able to streamline oral health care, facilitate workflow, increase oral health at a fraction of the current conventional cost, relieve dentist and dental auxiliary staff from routine and laborious tasks, but also ignite participatory in personalized oral health care. This narrative article review highlights recent dentistry digitalization encompassing technological advancement, limitations, challenges, and conceptual theoretical modern approaches in oral health prevention and care, particularly in ensuring the quality, efficiency, and strategic dental care in the modern era of dentistry.

Computer-Assisted Technologies in Arthroplasty: Navigating Your Way Today

Computer-assisted technologies that are used in arthroplasty include navigation, image-derived instrumentation (IDI), and robotics. Computer-assisted navigation improves accuracy and allows for real-time assessment of component positioning and soft-tissue tension. It is not clear whether the implementation of these technologies improves the clinical outcome of surgery. High cost and time demands have prevented the global implementation of computer-assisted technologies.

A Virtual Reality Environment to Visualize Three-Dimensional Patient-Specific Models by a Mobile Head-Mounted Display

INTRODUCTION

With the availability of low-cost head-mounted displays (HMDs), virtual reality environments (VREs) are increasingly being used in medicine for teaching and clinical purposes. Our aim was to develop an interactive, user-friendly VRE for tridimensional visualization of patient-specific organs, establishing a workflow to transfer 3-dimensional (3D) models from imaging datasets to our immersive VRE.

MATERIALS AND METHODS

This original VRE model was built using open-source software and a mobile HMD, Samsung Gear VR. For its validation, we enrolled 33 volunteers: morphologists (n = 11), trainee surgeons (n = 15), and expert surgeons (n = 7). They tried our VRE and then filled in an original 5-point Likert-type scale 6-item questionnaire, considering the following parameters: ease of use, anatomy comprehension compared with 2D radiological imaging, explanation of anatomical variations, explanation of surgical procedures, preoperative planning, and experience of gastrointestinal/neurological disorders. Results in the 3 groups were statistically compared using analysis of variance.

RESULTS

Using cross-sectional medical imaging, the developed VRE allowed to visualize a 3D patient-specific abdominal scene in 1 hour. Overall, the 6 items were evaluated positively by all groups; only anatomy comprehension was statistically significant different among the 3 groups.

CONCLUSIONS

Our approach, based on open-source software and mobile hardware, proved to be a valid and well-appreciated system to visualize 3D patient-specific models, paving the way for a potential new tool for teaching and preoperative planning.