Multi-class deep learning segmentation and automated measurements in periodontal sonograms of a porcine model

Ultrasound-based 3D bone modelling in computer assisted orthopedic surgery - a review and future challenges

Computer-assisted orthopedic surgery requires precise representations of bone surfaces. To date, computed tomography constitutes the gold standard, but comes with a number of limitations, including costs, radiation and availability. Ultrasound has potential to become an alternative to computed tomography, yet suffers from low image quality and limited field-of-view. These shortcomings may be addressed by a fully automatic segmentation and model-based completion of 3D bone surfaces from ultrasound images. This survey summarizes the state-of-the-art in this field by introducing employed algorithms, and determining challenges and trends. For segmentation, a clear trend toward machine learning-based algorithms can be observed. For 3D bone model completion however, none of the published methods involve machine learning. Furthermore, data sets and metrics are identified as weak spots in current research, preventing development and evaluation of models that generalize well.

DetSegDiff: A joint periodontal landmark detection and segmentation in intraoral ultrasound using edge-enhanced diffusion-based network

Individuals with malocclusion require an orthodontic diagnosis and treatment plan based on the severity of their condition. Assessing and monitoring changes in periodontal structures before, during, and after orthodontic procedures is crucial, and intraoral ultrasound (US) imaging has been shown a promising diagnostic tool in imaging periodontium. However, accurately delineating and analyzing periodontal structures in US videos is a challenging task for clinicians, as it is time-consuming and subject to interpretation errors. This paper introduces DetSegDiff, an edge-enhanced diffusion-based network developed to simultaneously detect the cementoenamel junction (CEJ) and segment alveolar bone structure in intraoral US videos. An edge feature encoder is designed to enhance edge and texture information for precise delineation of periodontal structures. Additionally, we employed the spatial squeeze-attention module (SSAM) to extract more representative features to perform both detection and segmentation tasks at global and local levels. This study used 169 videos from 17 orthodontic patients for training purposes and was subsequently tested on 41 videos from 4 additional patients. The proposed method achieved a mean distance difference of 0.17 ± 0.19 mm for the CEJ and an average Dice score of 90.1% for alveolar bone structure. As there is a lack of multi-task benchmark networks, thorough experiments were undertaken to assess and benchmark the proposed method against state-of-the-art (SOTA) detection and segmentation individual networks. The experimental results demonstrated that DetSegDiff outperformed SOTA approaches, confirming the feasibility of using automated diagnostic systems for orthodontists.

A Systematic Review on Caries Detection, Classification, and Segmentation from X-Ray Images: Methods, Datasets, Evaluation, and Open Opportunities

Dental caries occurs from the interaction between oral bacteria and sugars, generating acids that damage teeth over time. The importance of X-ray images for detecting oral problems is undeniable in dentistry. With technological advances, it is feasible to identify these lesions using techniques such as deep learning, machine learning, and image processing. Therefore, the survey and systematization of these methods are essential to determining the main computational approaches for identifying caries in X-ray images. In this systematic review, we investigated the primary computational methods used for classifying, detecting, and segmenting caries in X-ray images. Following the PRISMA methodology, we selected relevant studies and analyzed their methods, strengths, limitations, imaging modalities, evaluation metrics, datasets, and classification techniques. The review encompassed 42 studies retrieved from the Science Direct, IEEExplore, ACM Digital, and PubMed databases from the Computer Science and Health areas. The results indicate that 12% of the included articles utilized public datasets, with deep learning being the predominant approach, accounting for 69% of the studies. The majority of these studies (76%) focused on classifying dental caries, either in binary or multiclass classification. Panoramic imaging was the most commonly used radiographic modality, representing 29% of the cases studied. Overall, our systematic review provides a comprehensive overview of the computational methods employed in identifying caries in radiographic images and highlights trends, patterns, and challenges in this research field.

Use of ultrasound imaging for assessment of the periodontium: A systematic review

The objective of this study was to systematically review the literature regarding diagnostic applications of ultrasound imaging for evaluation of the periodontium in humans. The search was conducted on Medline, EMBASE, Web of Science, Scopus, Cochrane, and PubMed up to April 3, 2023. The studies included were exclusively human studies that assessed the periodontium with ultrasound (US) imaging (b-mode). Outcomes measured included alveolar bone level, alveolar bone thickness, gingival thickness, and blood flow quantification. References were imported to Covidence. Two reviewers conducted phases 1 and 2. The JBI risk assessment tool for cross-sectional studies was used. Extracted data included the transducer and measurements used and the study's outcomes. The search yielded 4892 studies after removing duplicates. From these, 25 studies were included and selected for extraction. Included studies retrieved outcomes from US examinations of the periodontal tissues. From the selected studies, 15 used US on natural teeth, 4 used US on implants, 2 used US on edentulous ridges, and 4 used color flow/power in US to evaluate the blood flow. The results of the present systematic review suggest that US might be a feasible and valuable diagnostic tool for the periodontium, with the potential to complement shortfalls of current radiographic technologies.

Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals

BACKGROUND AND AIM

Ultrasound (US) imaging is increasingly preferred over other more invasive modalities in preclinical studies using animal models. However, this technique has some limitations, mainly related to operator dependence. To overcome some of the current drawbacks, sophisticated data processing models are proposed, in particular artificial intelligence models based on deep learning (DL) networks. This systematic review aims to overview the application of DL algorithms in assisting US analysis of images acquired in in vivo preclinical studies on animal models.

METHODS

A literature search was conducted using the Scopus and PubMed databases. Studies published from January 2012 to November 2022 that developed DL models on US images acquired in preclinical/animal experimental scenarios were eligible for inclusion. This review was conducted according to PRISMA guidelines.

RESULTS

Fifty-six studies were enrolled and classified into five groups based on the anatomical district in which the DL models were used. Sixteen studies focused on the cardiovascular system and fourteen on the abdominal organs. Five studies applied DL networks to images of the musculoskeletal system and eight investigations involved the brain. Thirteen papers, grouped under a miscellaneous category, proposed heterogeneous applications adopting DL systems. Our analysis also highlighted that murine models were the most common animals used in in vivo studies applying DL to US imaging.

CONCLUSION

DL techniques show great potential in terms of US images acquired in preclinical studies using animal models. However, in this scenario, these techniques are still in their early stages, and there is room for improvement, such as sample sizes, data preprocessing, and model interpretability.

Overview of Ultrasound in Dentistry for Advancing Research Methodology and Patient Care Quality with Emphasis on Periodontal/Peri-implant Applications

BACKGROUND

Ultrasound is a non-invasive, cross-sectional imaging technique emerging in dentistry. It is an adjunct tool for diagnosing pathologies in the oral cavity that overcomes some limitations of current methodologies, including direct clinical examination, 2D radiographs, and cone beam computerized tomography. Increasing demand for soft tissue imaging has led to continuous improvements on transducer miniaturization and spatial resolution. The aims of this study are (1) to create a comprehensive overview of the current literature of ultrasonic imaging relating to dentistry, and (2) to provide a view onto investigations with immediate, intermediate, and long-term impact in periodontology and implantology.

METHODS

A rapid literature review was performed using two broad searches conducted in the PubMed database, yielding 576 and 757 citations, respectively. A rating was established within a citation software (EndNote) using a 5-star classification. The broad search with 757 citations allowed for high sensitivity whereas the subsequent rating added specificity.

RESULTS

A critical review of the clinical applications of ultrasound in dentistry was provided with a focus on applications in periodontology and implantology. The role of ultrasound as a developing dental diagnostic tool was reviewed. Specific uses such as soft and hard tissue imaging, longitudinal monitoring, as well as anatomic and physiological evaluation were discussed.

CONCLUSIONS

Future efforts should be directed towards the transition of ultrasonography from a research tool to a clinical tool. Moreover, a dedicated effort is needed to introduce ultrasonic imaging to dental education and the dental community to ultimately improve the quality of patient care.

Artificial Intelligence in Periodontology: A Scoping Review

Artificial intelligence (AI) is the development of computer systems whereby machines can mimic human actions. This is increasingly used as an assistive tool to help clinicians diagnose and treat diseases. Periodontitis is one of the most common diseases worldwide, causing the destruction and loss of the supporting tissues of the teeth. This study aims to assess current literature describing the effect AI has on the diagnosis and epidemiology of this disease. Extensive searches were performed in April 2022, including studies where AI was employed as the independent variable in the assessment, diagnosis, or treatment of patients with periodontitis. A total of 401 articles were identified for abstract screening after duplicates were removed. In total, 293 texts were excluded, leaving 108 for full-text assessment with 50 included for final synthesis. A broad selection of articles was included, with the majority using visual imaging as the input data field, where the mean number of utilised images was 1666 (median 499). There has been a marked increase in the number of studies published in this field over the last decade. However, reporting outcomes remains heterogeneous because of the variety of statistical tests available for analysis. Efforts should be made to standardise methodologies and reporting in order to ensure that meaningful comparisons can be drawn.