Reimagining blepharoplasty: the role of three-dimensional imaging and artificial intelligence in personalized eyelid surgery

Commentary 4.2 - The role of three-dimensional imaging in facial anatomical assessment

Sir: We thank the authors for their comments regarding our recent publication in Plastic and Reconstructive Surgery entitled “The Evolution of Photography and Three-Dimensional Imaging in Plastic Surgery.”1 We are excited to learn how they are using this technology to make volumetric measurements of the breast. First and foremost, we commend the application of three-dimensional imaging technology to improve clinical practice. For years, three-dimensional imaging has primarily been used for patient consultations, as a means of demonstrating potential surgical results to prospective patients. In this regard, its role has largely been a marketing tool to help convert patients. However, it is our belief that the role of three-dimensional imaging in plastic surgery extends well beyond marketing and ultimately will be most valuable as a clinical tool. This was our intention during our early studies with three-dimensional photography and breast surgery when Drs. Nolan Karp, Mihye Choi, and others in our group coined the term “mammometrics.” The concept of mammometrics, which was derived from the concept of cephalometrics, aimed to provide objective three-dimensional measurements that could be used to plan and assess cases of aesthetic and reconstructive breast surgery. Numerous other groups have since studied the potential clinical benefits of three-dimensional scanning technology in plastic surgery, and your comments further validate these efforts. One critical point that the authors raise is the importance of surgeons being able to integrate this into practice with relative ease. Previously, many of these modalities were largely cost prohibitive, thus limiting widespread adoption. However, the structure three-dimensional scanner that you describe is appealing given its low cost and handheld nature. In addition, many other similar products are currently on the market, including personal phones that are soon going to be equipped with three-dimensional scanners. Computer software is also becoming increasingly user-friendly and relatively inexpensive, or even open-sourced, such as MeshLab, which the authors describe. We agree that adoption of this technology will only continue to grow in the coming years, and predict that this will become standard practice for photographic assessment of our plastic surgery patients. DISCLOSURE None of the authors has a financial interest in any of the products or devices mentioned in this communication. Oren M. Tepper, M.D.Jason Weissler, M.D.Montefiore Medical CenterAlbert Einstein College of MedicineBronx, N.Y.

The role of three-dimensional imaging in optimizing diagnosis, classification and surgical treatment of hepatocellular carcinoma with portal vein tumor thrombus

Role of three-dimensional imaging in operative planning for hilar cholangiocarcinoma

Three-dimensional (3D) imaging is revolutionising patient assessment, diagnosis, management and treatment planning. Restorative dentistry is using optical scanning such as the computer aided design/computer aided manufacture systems to help with tooth preparation design and construction of fixed prosthodontics. Other specialties in dentistry are frequently employing cone beam computed tomography (CBCT) to facilitate 3D imaging. This article outlines how CBCT and 3D sterophotogrammetry have been used in the management of cleft lip and palate with reference to the cleft team based at Glasgow Dental Hospital.

Background: In the field of endodontics, accurate diagnosis is pivotal for successful treatment outcomes. This randomized controlled trial (RCT) explores the potential of cone-beam computed tomography (CBCT) as a tool to enhance diagnostic accuracy in endodontic procedures Materials and Methods: An RCT was conducted with a sample of 120 patients presenting with endodontic issues. The patients were divided into two groups: Group A received traditional two-dimensional radiography, while group B underwent CBCT scans. The diagnostic accuracy was assessed by comparing the radiographic findings with the clinical evaluation by experienced endodontists. Results: The results indicated a significant improvement in diagnostic accuracy in the CBCT group (group B) with an arbitrary value of 88% accuracy, compared with the traditional radiography group (group A) with only 65% accuracy. The CBCT group showed a clearer visualization of root canal anatomy, periapical lesions, and the presence of additional canals, contributing to the enhanced diagnostic capability Conclusion: This RCT demonstrates that CBCT significantly enhances diagnostic accuracy in endodontics compared with traditional two-dimensional radiography. The improved visualization of root canal anatomy and periapical regions allows for more precise treatment planning, ultimately leading to better treatment outcomes.

The Role of Three-Dimensional Imaging in Evaluation of the Sinonasal Mass

Dear Editor Intrauterine Contraceptive Devices (IUDs) are a form of long-term, reversible, and safe contraception, which are commonly used worldwide. A regular medical check-up is necessary to determine its position within the uterus, up to six months after IUD insertion. The complications of translocation have a wide range from displacement of IUD in myometrium to uterine perforation. Predisposing factors which are considered to be associated with dislocation include postpartum insertion, inexperienced operator insertion technique, and position of the uterus (1). Clinical history, physical examination, and transvaginal ultrasonography (US) are common procedures for the evaluation of IUDs and related complications. Although two-dimensional (2D) US (2DUS) is a routine modality in practice, it has a limited role for verifying two arms of IUD within the same plane; therefore, it may fail to detect IUD displacement (2). Furthermore, the most currently introduced hormonal IUDs can be demonstrated only by a vague shadow and might be remained unnoticed in 2DUS due to their low echogenicity (3). Recently, the availability of advanced US modalities has changed the management of IUDs by optimal evaluation of entire uterine cavity. Coronal view on three-dimensional (3D) US (3DUS) is particularly helpful to visualize the shaft and both horizontal arms in a single plane. In symptomatic patients with pelvic pain or abnormal bleeding, many IUDs, which appeared to be placed correctly or low on 2DUS, were confirmed to be imbedded, at least in part, within the myometrium with further investigations using hysteroscopy or 3DUS (4). The 3DUS is extremely useful in the management of IUDs by coronal views of the uterus and 3D-reconstructed views of the endometrium and adjacent myometrium. Many studies demonstrated the overall outstanding effectiveness of 3DUS in determining IUDs’ location, particularly for symptomatic patients with complications or patients with hormonal IUDs. Valsky et al. reported that 3DUS has a great value in symptomatic patients when the location of IUD cannot be correctly identified with traditional 2DUS (5). Bonilla-Musoles et al. designed a comparative study for the identification and location of IUDs in 66 asymptomatic women by 2DUS and 3DUS. While position of all IUDs were identified accurately with 3DUS, 2DUS failed to identify the type of IUD in 9% as well as the position of IUDs in 3%, and misidentified IUDs in 12% of patients, which was later confirmed on 3DUS (2). Lee et al. claimed the complete visualization of IUDs in 95% of patients on 3DUS vs 64% on 2DUS (6). Figure 3. In third patient, descended t-shaped intrauterine device IUD was located in the with its left arm in the cervical substance of the intrauterine device in the cervical substance (A, B). In this paper, we present 2D and 3D images of three patients with a history of IUD, placed one to three years earlier, with complaints of abdominal pain or spotting. Low positioned IUDs were identified on 2DUS. Further investigation by 3DUS was required in these symptomatic patients in order to determine the exact location of IUDs. The 3DUS was performed using 3DXI (ACCUVIX XQ, Medison, South Korea) US device with a 6.5 MHz transvaginal probe. The 2DUS and 3DUS images of patients are compared in Figures 1 - ​-33. Figure 1. In first patient, t-shaped intrauterine device (IUD) was revealed with a vertical body and flexible arms clearly not protruding beyond the confines of the endometrial echo (A, B). Figure 2. A, A low-lying of intrauterine device (IUD) in the lower uterine segment using standard two-dimensional imaging in second patient; B, The results of three-dimensional ultrasonography revealed t-shaped intrauterine device dislocated in endometrial cavity ... Comparing two images of 2DUS and 3DUS demonstrated the additional information helping in identifying the cause of abdominal pain or spotting. Sites of IUD translocation vary in terms of their clinical significance and selection of subsequent therapeutic plan. Although menorrhagia and intermenstrual bleeding have been considered as a common adverse effect of IUD placement, pelvic pain and bleeding should raise the possibility of dislodgement, perforation, or passage. The 3DUS has a crucial role in the management of both asymptomatic patients and those with suspected complications, whilst IUDs location may remain unnoticed on physical examination and 2DUS.

Advancements in three-dimensional (3D) surface imaging and artificial intelligence (AI) are transforming dental research and clinical practice by providing high-precision, non-invasive tools for diagnosis, treatment planning, and outcome prediction. Traditional imaging methods, while effective, often lack dimensional detail and involve radiation exposure, whereas 3D imaging and AI offer improved safety and accuracy. This systematic review aims to evaluate the efficacy, safety, and practical applications of 3D surface imaging and AI technologies in dentistry, with a focus on orthodontics, maxillofacial surgery, and diagnostic practices.Following PRISMA guidelines, a comprehensive literature search was conducted across databases including PubMed, Cochrane Library, and Google Scholar. Studies were selected based on criteria such as population, intervention type, and outcome relevance. Data extraction and quality assessment were performed using standardized tools, and bias was evaluated with the Cochrane Risk of Bias Tool for randomized controlled trials and ROBINS-I for non-randomized studies.The review included 50 studies encompassing various imaging technologies (e.g., structured light scanning, laser scanning) and AI applications (e.g., convolutional neural networks). Findings indicate significant improvements in diagnostic accuracy, patient-specific modeling, and clinical workflow efficiency. Benefits include reduced radiation exposure, enhanced diagnostic precision, and increased affordability, although challenges remain in terms of operational complexity, cost, and potential AI biases.3D surface imaging and AI represent substantial advancements in dental practice, enabling precise diagnostics, tailored treatment, and improved patient outcomes. Future research should focus on refining AI algorithms, standardizing protocols, and developing accessible, portable 3D imaging devices to expand these technologies’ reach in clinical settings.

This article compares the diagnostic quality of three groups of dentists with different work experience and different specialties using an artificial intelligence (AI) system. The study proceeded in several stages: 1) studying AI systems used in dentistry; 2) selecting a patient and conducting clinical and X-ray examinations with subsequent processing of the obtained AI data; 3) conducting research among dentists of various specialties and work experience; 4) comparing the results obtained with AI. The “Diagnocat system” (Russia) was selected for the study and an X-ray report of a pre-selected image was performed (which was suitable based on the results of a clinical examination and X-ray analysis by a dentist and AI analysis), dentists were asked to study this CT scan (a program was provided to view the image in three-dimensional image) and clinical photographs of the patient, then The responses of 60 dentists were analyzed. Cases of overdiagnosis by dental doctors have been identified, which tells us about a medical error that can be eliminated when using AI. According to the study, patients who come to a dentist-therapist with 5 to 15 years of work experience receive a better diagnosis.

Go is an ancient board game in which two players, by placing "stones" on a square grid, aim to surround more territory than the opponent. It was a pivotal moment in the history of humankind when AlphaGo Master, a computer program developed by DeepMind Technologies from UK, defeated professional Go player Ke Jie in three games of Go during the 2017 Future of Go Summit in Wuzhen, China.

Artificial intelligence vs. semi-automated segmentation for assessment of dental periapical lesion volume index score: A cone-beam CT study

Precision medicine technologies have the potential to revolutionise the way melanoma is detected and managed Melanoma is a deadly form of skin cancer and represents a global health challenge, particularly for Australians and our health system.1 Early detection of melanoma is associated with lower morbidity and mortality and lower health care costs compared with late detection, when primary lesions are thicker or have metastasised. It currently occurs through scheduled appointments or opportunistic skin checks, with general practitioners and dermatologists observing the skin surface and focusing on areas of concern using dermoscopy. Early detection remains an important strategy to reduce melanoma mortality,2 improve melanoma survival,3 and has been shown to be cost-effective.4, 5 However, novel precision medicine technologies for melanoma diagnosis, including three-dimensional (3D) total body imaging, teledermoscopy, proteomics-based blood and tissue biomarkers, and artificial intelligence (AI)-driven algorithms for lesion recognition (Box), are rapidly changing this field. (A) Three-dimensional (3D) total body skin surface macro imaging. (B) Teledermoscopy: the patient photographs suspicious lesions using a smartphone dermatoscope attachment and sends the images to the clinician for remote diagnosis. (C) Scarless biopsy (tape stripping) reduces unnecessary excisions. (D) Artificial intelligence (AI)-driven algorithms are used for lesion recognition and support for clinical decision making. The Australian Centre of Excellence in Melanoma Imaging and Diagnosis (ACEMID) program received a grant from the Australian Cancer Research Foundation in 2018 to "reconceive" early detection of melanoma. ACEMID will develop a network of next-generation 3D skin imaging technology integrated with telemedicine, and aims to implement equitable, accessible and cost-effective early detection protocols and procedures. To do so, it will refine, digitally integrate, and optimise the application of novel imaging technologies through its research streams over the next 5 years. However, broader implementation, scalability and reimbursement will be likely hamstrung by the lack of a fit-for-purpose health technology assessment (HTA) framework for precision medicine technologies.6 In this perspective article, we will outline the current challenges and potential recommendations for Australian HTA agencies such as the Medical Services Advisory Committee (MSAC) and the Pharmaceutical Benefits Advisory Committee (PBAC) to assess the value of precision medicine diagnostics. Teledermoscopy uses the camera on an individual's mobile phone with an attachment that allows the user to take magnified photographs of skin lesions. Dermoscopic-quality pictures of skin lesions can be sent to a specialist for triage. The "store and forward" technology enables provision of dermatologic care services remotely at a later time point for diagnosing and advising management of skin cancer when an in-person clinical visit is not possible.7 Theoretical advantages of teledermoscopy include close inspections of specific skin lesions to improve melanoma early detection for people in underserved or remote areas. Some of the challenges of teledermoscopy include poor image quality and/or lesion selection when photos are taken by patients, inadequate clinical data, and potentially the loss of a local patient–clinician relationship if digital skin assessment is transferred to a metropolitan-based dermatologist. Technology for total body photography has recently developed from a sequence of separate two-dimensional (2D) images of different parts of the skin's surface to 3D imaging. The ACEMID VECTRA WB360 3D whole body imaging system (Canfield Scientific, Parsippany, NJ, USA) consists of 92 cameras that simultaneously capture nearly the entire skin surface, including curved surfaces in macro-quality resolution, and then constructs a 3D digital avatar of the individual,8 allowing the precise location of moles across explorations. This image can ensure precise documentation of the exact anatomical location of each lesion to identify changes over time.8 The clinician can review sequential imaging sessions consisting of 3D imaging and determine whether dermoscopy, biopsy or excision is necessary. The advantages of 3D imaging include rapid acquisition of the images,8 software that allows integration of dermoscopy for individual lesions into the 3D avatar, with each lesion given an individual identifier location in the 3D space; and automated assessment of size, colour variation and border irregularity, allowing clinicians to monitor skin lesion stability. The challenge is to enable access to 3D imaging in rural and remote Australia for equitable health service delivery. In clinical practice, GPs and dermatologists every so often face the diagnostic dilemma of ambiguous pigmented and non-pigmented lesions.9 Clinicians must balance the risk of leaving a possible melanoma to grow further, with the removal of a benign lesion and related medico-legal, patient and health system concerns, including unwarranted excisions and overdiagnosis.10 Therefore, the ACEMID team is pursuing the idea of a scarless biopsy in the domain of proteomic signatures under an "omics"-based protocol. During a scarless biopsy, the stratum corneum (upper layers of skin) is collected by consecutive application and removal of adhesive discs, applied to the skin's surface (ie, tape stripping) supported by protein extraction and quantification using mass spectrometry. An advantage of scarless biopsy will be the ability to add to the sensitivity and specificity of existing diagnostic and prognostic markers (unpublished data). AI-enabled medical technologies have the potential to support clinical decision making. The aim of AI is to surpass human cognitive functioning such that automatic decisions can be made autonomously, yet still achieve a reliable diagnostic assessment. Then, sophisticated AI-driven algorithms that evaluate imaging data banks, genomic information, electronic health records, and sociodemographic data can predict diagnosis, prognosis, and optimal management for individuals. For example, the QRISK risk prediction algorithm for future risk of cardiovascular disease uses data from health records,11 and the diabetic retinopathy algorithm uses an imaging data bank of colour fundus photographs.12 These have been approved for clinical use by the National Institute for Health and Care Excellence (QRISK) and the United States Food and Drug Administration (diabetic retinopathy algorithm). In melanoma diagnosis, AI-driven algorithms reported in laboratory studies have similar diagnostic accuracy relative to that of dermatologists under test conditions.13, 14 Misdiagnosis, overtreatment and under-reporting are current issues in melanoma and skin cancer care. A recent study found that about 55% of melanoma cases (invasive melanoma, 22%) were overdiagnosed10 and associated with iatrogenic harms and increased health care costs. Melanoma misdiagnosis accounts for a greater number of pathology and dermatology malpractice claims compared with other cancers.15 Recent studies report improved accuracy of skin cancer classification by AI-driven approaches versus dermatologists/pathologists using standard clinical practices.16-18 The potential for harm clearly exists in the current standard health care services due to a high rate of false-positive diagnoses, adverse effects, overdiagnosis, and potential overtreatment,19 but as to whether this is improved or worsened with precision medicine technologies remains to be seen. However, using AI for decision making in the clinical setting needs further study, taking into account the contextual and temporal component of the biologic ecosystem of skin lesions, as well as clinician–AI–patient interactions.18 The ACEMID technology20 proposes automated lesion identification and diagnosis to be used either in a triage capacity before clinical review, whereby lesions suspicious for melanoma would be prioritised, or after clinical review acting as an independent second opinion.21 Place in the clinical pathway. ACEMID technologies can have multiple places on the clinical pathway. They can be predictive and/or prognostic, may be used as a triage tool for high risk lesions and/or high risk individuals, a diagnostic support tools as either an add-on or a replacement test. They can also be used as one-off or multiple times along the clinical pathway. By providing a baseline, quality assurance becomes possible. Uncertainty. Multilevel uncertainty in clinical and economic models is likely to arise from wide precision estimates of effect due to very small sample sizes of like-individuals. ACEMID technologies may stratify people into small subgroups based on imaging, genetic or protein signatures. Uncertainty in estimates of effectiveness may also result from the dynamic nature of the technologies that are likely to improve in their precision and relevance over time. For example, AI-driven algorithms are continually building upon new image datasets to improve their diagnostic sensitivity and specificity. Applications with frequently updated interfaces and changeable hosting platforms (eg, those providing automated skin lesion assessment or clinician-assessed teledermoscopy) also represent technologies where small, yet frequent changes may alter the uncertainty around the technology's efficacy, effectiveness or cost-effectiveness. Equity. The disproportionate use of new precision medicine technologies for individuals with higher income, compared with individuals with lower socio-economic level, may widen the current gap in melanoma health outcomes. Although we have seen a growth in digital health care throughout the coronavirus disease 2019 (COVID-19) pandemic, many patients could be excluded from these health technology-driven services if disproportionate access continues to be related to socio-economic status. Interconnected with this concern is a bias that many algorithms have so far excluded skin of colour and uncommon presentations of melanoma and other rare malignant skin neoplasms in their datasets and image banks.22 Shelf-life of the guidance. Finally, the duration of time in which the effectiveness evidence remains current for precision medicine diagnostics may be short before they are replaced with the next iteration or model. For example, other systems are in development that will provide total body images with dermoscopy quality in the future.23 As precision medicine technologies are increasingly used, the methods and processes of HTA will need to adapt to maintain their objective of evaluating whether a health technology investment is good value for money. Precision medicine technologies have the potential to revolutionise the way melanoma is detected and managed. ACEMID focuses on 3D imaging and telemedicine research, in which Australia will lead the evidence generation of diagnostic efficacy, and real-world implementation into primary and specialist care. To assess the value of these precision medicine technologies, HTA agencies will need to accommodate evidence from different study designs, dynamic modelling from evolving biomarkers and algorithms, a mechanism for assessing the impact on health equity, and rapid updates as technology changes. Rachael Morton is supported by a National Health and Medical Research Council (NHMRC) Emerging Leadership-2 Fellowship (1194703). We acknowledge the Australian Cancer Research Foundation for financial support to the ACEMID program. Victoria Mar received the NHMRC Clinical Trials and Cohort Studies Grant (2001517), and Monika Janda received the NHMRC Centre of Research Excellence (2006551) and the NHMRC Synergy Grant (2009923). We thank the following ACEMID Team members for their contribution to this manuscript: Anne Cust (Daffodil Centre, University of Sydney and Cancer Council NSW), Joanne Aitken (Cancer Council Queensland); Richard Scolyer (Melanoma Institute Australia, University of Sydney), Pascale Guitera (Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital), Liam Caffery (Centre for Online Health, University of Queensland), Rory Wolfe (Monash University), and Graham Mann (John Curtin School of Medical Research, Australian National University). Open access publishing facilitated by The University of Sydney, as part of the Wiley - The University of Sydney agreement via the Council of Australian University Librarians. H Peter Soyer is a shareholder of MoleMap NZ and E-Derm-Consult and undertakes regular teledermatological reporting for both companies; is a medical consultant for Canfield Scientific, MoleMap Australia, Blaze Bioscience and Revenio Research; and is a medical advisor for First Derm. Not commissioned; externally peer reviewed.

The aim of this systematic review was to comprehensively analyse recent studies on the application of artificial intelligence (AI) in dental implantology. The PRISMA guidelines were followed. Five databases were accessed: Scopus, Web of Science, MEDLINE/PubMed, IEEE Xplore, and JSTOR. Documents published between 2018 and October 15, 2024 relating to AI and implantology were considered. Exclusions encompassed reviews, opinion articles, books, conference references, studies using AI as a supplementary method, AI for teaching implant dentistry, and AI for implant fabrication, prothesis, or design. A total of 120 relevant papers were included. Risk of bias was assessed using PROBAST. Findings demonstrated extensive utilization of AI in various aspects of dental implantology: guided surgery, diagnosis, classification of oral structures, bone classification, classification of dental restorations, implant classification, implant planning, and implant prognosis. Deep learning algorithms were employed in 89.2% of studies, predominantly utilizing image data (72.0% two-dimensional images and 28.0% three-dimensional images). Publications doubled in 2022 compared to the previous year and have remained consistent since. Despite growth, the field remains relatively underdeveloped. However, with advancements in technology and data quality, substantial progress is anticipated in forthcoming years. Remarkably, 11 studies were found to have a high risk of bias.

Preprocedural simulation can be an important tool for patient counseling and setting patient expectations in a plastic surgeon's office. Simulation technologies are used widely in plastic surgery and can involve expensive three-dimensional imaging suites that allow the plastic surgeon to alter patient photographs to simulate postprocedural results. Artificial intelligence (AI) is gaining traction in many facets of the plastic surgery world. However, AI has not been significantly utilized in preprocedural simulation. AI represents a new frontier in plastic surgery simulation as inexpensive AI "generative fill" technology can instantly alter and rejuvenate specifically demarcated areas of patient photographs to render realistic postprocedural results. The authors aimed to assess the feasibility of using AI to simulate whole-face rejuvenation with soft tissue fillers by performing injection simulations on real patient photographs. This article lays the foundation for future studies applying more sophisticated AI models in plastic surgery preprocedural simulation.