Artificial Intelligence for Mohs and Dermatologic Surgery: A Systematic Review and Meta-Analysis

BACKGROUND

Over the past decade, several studies have shown that potential of artificial intelligence (AI) in dermatology. However, there has yet to be a systematic review evaluating the usage of AI specifically within the field of Mohs micrographic surgery (MMS).

OBJECTIVE

In this review, we aimed to comprehensively evaluate the current state, efficacy, and future implications of AI when applied to MMS for the treatment of nonmelanoma skin cancers (NMSC).

MATERIALS AND METHODS

A systematic review and meta-analysis was conducted following PRISMA guidelines across several databases, including PubMed/MEDLINE, Embase, and Cochrane libraries. A predefined protocol was registered in PROSPERO, with literature search involving specific keywords related to AI and Mohs surgery for NMSC.

RESULTS

From 23 studies evaluated, our results find that AI shows promise as a prediction tool for precisely identifying NMSC in tissue sections during MMS. Furthermore, high AUC and concordance values were also found across the various usages of AI in MMS, including margin control, surgical recommendations, similarity metrics, and in the prediction of stage and construction complexity.

CONCLUSION

The findings of this review suggest promising potential for AI to enhance the accuracy and efficiency of Mohs surgery, particularly for NMSC.

Artificial intelligence for nonmelanoma skin cancer

Nonmelanoma skin cancers (NMSCs) are among the top five most common cancers globally. NMSC is an area with great potential for novel application of diagnostic tools including artificial intelligence (AI). In this scoping review, we aimed to describe the applications of AI in the diagnosis and treatment of NMSC. Twenty-nine publications described AI applications to dermatopathology including lesion classification and margin assessment. Twenty-five publications discussed AI use in clinical image analysis, showing that algorithms are not superior to dermatologists and may rely on unbalanced, nonrepresentative, and nontransparent training data sets. Sixteen publications described the use of AI in cutaneous surgery for NMSC including use in margin assessment during excisions and Mohs surgery, as well as predicting procedural complexity. Eleven publications discussed spectroscopy, confocal microscopy, thermography, and the AI algorithms that analyze and interpret their data. Ten publications pertained to AI applications for the discovery and use of NMSC biomarkers. Eight publications discussed the use of smartphones and AI, specifically how they enable clinicians and patients to have increased access to instant dermatologic assessments but with varying accuracies. Five publications discussed large language models and NMSC, including how they may facilitate or hinder patient education and medical decision-making. Three publications pertaining to the skin of color and AI for NMSC discussed concerns regarding limited diverse data sets for the training of convolutional neural networks. AI demonstrates tremendous potential to improve diagnosis, patient and clinician education, and management of NMSC. Despite excitement regarding AI, data sets are often not transparently reported, may include low-quality images, and may not include diverse skin types, limiting generalizability. AI may serve as a tool to increase access to dermatology services for patients in rural areas and save health care dollars. These benefits can only be achieved, however, with consideration of potential ethical costs.

Application of artificial intelligence in oculoplastics

Oculoplastics is a subspecialty of ophthalmology/dermatology concerned with eyelid, orbital, and lacrimal diseases. Artificial intelligence (AI), with its powerful ability to analyze large data sets, has dramatically benefited oculoplastics. The cutting-edge AI technology is widely applied to extract ocular parameters and to use these results for further assessment, such as screening and diagnosis of blepharoptosis and predicting the progression of thyroid eye disease. AI also assists in treatment procedures, such as surgical strategy planning in blepharoptosis. High efficiency and high reliability are the most apparent advantages of AI, with promising prospects. The possibilities of AI in oculoplastics may lie in three-dimensional modeling technology and image generation. We retrospectively summarize AI applications involving eyelid, orbital, and lacrimal diseases in oculoplastics, and we also examine the strengths and weaknesses of AI technology in oculoplastics.

The use of artificial intelligence in reconstructive surgery for head and neck cancer: a systematic review

OBJECTIVES

The popularity of artificial intelligence (AI) in head and neck cancer (HNC) management is increasing, but postoperative complications remain prevalent and are the main factor that impact prognosis after surgery. Hence, recent studies aim to assess new AI models to evaluate their ability to predict free flap complications more effectively than traditional algorithms. This systematic review aims to summarize current evidence on the utilization of AI models to predict complications following reconstructive surgery for HNC.

METHODS

A combination of MeSH terms and keywords was used to cover the following three subjects: "HNC," "artificial intelligence," and "free flap or reconstructive surgery." The electronic literature search was performed in three relevant databases: Medline (Ovid), Embase (Ovid), and Cochrane. Quality appraisal of the included study was conducted using the TRIPOD Statement.

RESULTS

The review included a total of 5 manuscripts (n = 5) for a total of 7524 patients. Across studies, the highest area under the receiver operating characteristic (AUROC) value achieved was 0.824 by the Auto-WEKA model. However, only 20% of reported AUROCs exceeded 0.70. One study concluded that most AI models were comparable or inferior in performance to conventional logistic regression. The highest predictors of complications were flap type, smoking status, tumour location, and age.

DISCUSSION

Some models showed promising results. Predictors identified across studies were different than those found in existing literature, showing the added value of AI models. However, the algorithms showed inconsistent results, underlying the need for better-powered studies with larger databases before clinical implementation.

Management of periocular keratinocyte carcinomas with Mohs micrographic surgery and predictors of complex reconstruction: a retrospective study

BACKGROUND

Skin cancer is the most frequent cancer worldwide and the most frequent periocular tumor. Keratinocyte Carcinomas (KC) located in periorificial areas, such as periocular tumors, are considered high-risk tumors. Mohs Micrographic Surgery (MMS) is considered the first line for the treatment of high-risk KC, providing a lower recurrence rate than conventional wide excision.

OBJECTIVE

To describe the clinical-pathological features of periocular KC treated with MMS in a tertiary university center in Chile.

METHODS

A single-center, retrospective study of patients with KC located on the periocular area, that underwent MMS between 2017‒2022. MMS details were recorded.

RESULTS

One hundred thirteen patients with periocular carcinomas were included. The mean age was 59 ± 13 years; 52% were women. The most frequent location was the medial canthus (53%), followed by the lower eyelid (30.1%). The most frequent BCC histology was the nodular variant (59.3%). Regarding MMS, the average number of stages was 1.5 ± 0.7, and 54% of the cases required only 1 stage to achieve clear margins. To date, no recurrence has been reported. Tumors larger than 8.5 mm in largest diameter or 43.5 mm2 were more likely to require complex reconstruction.

STUDY LIMITATIONS

Retrospective design and a relatively low number of patients in the SCC group. Possible selection bias, as larger or more complex cases, may have been referred to oculoplastic surgeons directly.

CONCLUSION

The present study confirms the role of MMS for the treatment of periocular KCs. Periocular KCs larger than 8.5 mm might require complex reconstruction. These results can be used to counsel patients during pre-surgical visits.

Lifestyle Influence on Mild Cognitive Impairment Progression: A Decision Tree Prediction Model Study

Purpose

This study assessed the influences of different lifestyle on mild cognitive impairment (MCI) progression and established a decision tree prediction model to analyse their predictive significance on MCI progression incidence.

Patients and Methods

From October 2015 to February 2020,330 patients with MCI were recruited, and demographic and lifestyle information collected. They were followed up for 19.04 ± 10.227 months. Cognitive function was assessed using the Mini-Mental State Examination Scale every 6 months, and they were divided into MCI stable group and MCI progression group.

Results

The Kaplan Meier survival analysis showed an overall cohort survival rate of 33.2%; the annual conversion rate of MCI progression was 20%. Physical exercise, social engagement, high-fat diet, age, napping, and tea drinking were decision tree prediction model nodes. Hobbies were the most important factor for predicting MCI progression. The MCI progression probability rates were: with hobbies 26.829% (44 cases), without hobbies 57.831% (96 cases); for those withot hobbies, with physical exercise 43.077% (28 cases) without physical exercise 72.340% (68 cases); for those without hobbies with physical exercise and social engagement 20.000% (4 cases), without social engagement 53.333% (24 cases); for those without hobbies, physical exercises and social engagement and with nap habits 48.485% (16 cases), without nap habits 66.667% (8 cases). The decision tree prediction model AUC for predicting the MCI progression receiver operating characteristic curve was 0.737 (95% confidence interval: 0.685-0.785) (75.71% sensitivity, 71.75% specificity, P < 0.001.

Conclusion

Hobbies, physical exercise, social engagement, napping, and drinking tea can help prevent MCI progression, while a high-fat diet may exacerbate MCI progression. In this study the rule with the lowest MCI progress probability for those who had hobbies, high-fat diet, and social engagement. And the decision tree model had good prediction efficiency.

State of the Evidence for Facial Skin Cancer Reconstruction

This review provides a comprehensive presentation of the evidence available in facial reconstruction post-Mohs micrographic surgery. Given the large diversity in post-Mohs reconstruction, there are limited guidelines in the management of defects. The aim of the present work is to provide a review of the best evidence as it pertains to several considerations in facial reconstruction. Data suggests that Mohs micrographic surgery and many reconstructive procedures can be performed as outpatient procedures under local anesthesia, with narcotic pain medication only given in certain patient populations following a minority of reconstructive procedures. Perioperative and topical antibiotics are generally not indicated. Aspirin and warfarin can generally be continued for most reconstructive procedures, but clopidogrel and novel anticoagulants may predispose to increased bleeding complications. Delayed reconstruction appears to be safe, although data are discordant on this topic. No specific wound closure technique or suture choice appears to be consistently superior. Given the lack of robust comparative studies, consistent methodology, and variable defect sizes/locations, no robust evidence-based guidelines can be generated for reconstruction techniques of facial subsites.

Orbital and eyelid diseases: The next breakthrough in artificial intelligence?

Orbital and eyelid disorders affect normal visual functions and facial appearance, and precise oculoplastic and reconstructive surgeries are crucial. Artificial intelligence (AI) network models exhibit a remarkable ability to analyze large sets of medical images to locate lesions. Currently, AI-based technology can automatically diagnose and grade orbital and eyelid diseases, such as thyroid-associated ophthalmopathy (TAO), as well as measure eyelid morphological parameters based on external ocular photographs to assist surgical strategies. The various types of imaging data for orbital and eyelid diseases provide a large amount of training data for network models, which might be the next breakthrough in AI-related research. This paper retrospectively summarizes different imaging data aspects addressed in AI-related research on orbital and eyelid diseases, and discusses the advantages and limitations of this research field.

Predicting Mohs surgery complexity by applying machine learning to patient demographics and tumor characteristics

Mohs micrographic surgery (MMS) is considered the gold standard for difficult-to-treat malignant skin tumors, whose incidence is on the rise. Currently, there are no agreed upon classifiers to predict complex MMS procedures. Such classifiers could enable better patient scheduling, reduce staff burnout and improve patient education. Our goal was to create an accessible and interpretable classifier(s) that would predict complex MMS procedures. A retrospective study applying machine learning models to a dataset of 8644 MMS procedures to predict complex wound reconstruction and number of MMS procedure stages. Each procedure record contained preoperative data on patient demographics, estimated clinical tumor size prior to surgery (mean diameter), tumor characteristics and tumor location, and postoperative procedure outcomes included the wound reconstruction technique and the number of MMS stages performed in order to achieve tumor-free margins. For the number of stages complexity classification model, the area under the receiver operating characteristic curve (AUROC) was 0.79 (good performance), with model accuracy of 77%, sensitivity of 71%, specificity of 77%, positive prediction value (PPV) of 14% and negative prediction value (NPV) of 98%. The results for the wound reconstruction complexity classification model were 0.84 for the AUROC (excellent performance), with model accuracy of 75%, sensitivity of 72%, specificity of 76%, PPV of 39% and NPV of 93%. The ML models we created predict the complexity of the components that comprise the MMS procedure. Using the accessible and interpretable tool we provide online, clinicians can improve the management and well-being of their patients. Study limitation is that models are based on data generated from a single surgeon.

The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review

Artificial intelligence (AI) is a subset of computer science dealing with the development and training of algorithms that try to replicate human intelligence. We report a clinical overview of the basic principles of AI that are fundamental to appreciating its application to ophthalmology practice. Here, we review the most common eye diseases, focusing on some of the potential challenges and limitations emerging with the development and application of this new technology into ophthalmology.

Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery

INTRODUCTION

Machine learning (ML) is a set of models and methods that can detect patterns in vast amounts of data and use this information to perform various kinds of decision-making under uncertain conditions. This review explores the current role of this technology in plastic surgery by outlining the applications in clinical practice, diagnostic and prognostic accuracies, and proposed future direction for clinical applications and research.

METHODS

EMBASE, MEDLINE, CENTRAL and ClinicalTrials.gov were searched from 1990 to 2020. Any clinical studies (including case reports) which present the diagnostic and prognostic accuracies of machine learning models in the clinical setting of plastic surgery were included. Data collected were clinical indication, model utilised, reported accuracies, and comparison with clinical evaluation.

RESULTS

The database identified 1181 articles, of which 51 articles were included in this review. The clinical utility of these algorithms was to assist clinicians in diagnosis prediction (n=22), outcome prediction (n=21) and pre-operative planning (n=8). The mean accuracy is 88.80%, 86.11% and 80.28% respectively. The most commonly used models were neural networks (n=31), support vector machines (n=13), decision trees/random forests (n=10) and logistic regression (n=9).

CONCLUSIONS

ML has demonstrated high accuracies in diagnosis and prognostication of burn patients, congenital or acquired facial deformities, and in cosmetic surgery. There are no studies comparing ML to clinician's performance. Future research can be enhanced using larger datasets or utilising data augmentation, employing novel deep learning models, and applying these to other subspecialties of plastic surgery.

Review of Machine Learning in Predicting Dermatological Outcomes

Artificial intelligence is a broad branch of computer science that has garnered significant interest in the field of medicine because of its problem solving, decision making and pattern recognition abilities. Machine learning, a subset of artificial intelligence, hones in on the ability of computers to receive data and learn for themselves, manipulating algorithms as they organize the information they are processing. Dermatology is at a particular advantage in the implementation of machine learning due to the availability of large clinical image databases that can be used for machine training and interpretation. While numerous studies have implemented machine learning in the diagnostic aspect of dermatology, less research has been conducted on the use of machine learning in predicting long-term outcomes in skin disease, with only a few studies published to date. Such an approach would assist physicians in selecting the best treatment methods, save patients' time, reduce treatment costs and improve the quality of treatment overall by reducing the amount of trial-and-error in the treatment process. In this review, we aim to provide a brief and relevant introduction to basic artificial intelligence processes, and to consolidate and examine the published literature on the use of machine learning in predicting clinical outcomes in dermatology.

Artificial Intelligence Applications in Dermatology: Where Do We Stand?

Artificial intelligence (AI) has become a progressively prevalent Research Topic in medicine and is increasingly being applied to dermatology. There is a need to understand this technology's progress to help guide and shape the future for medical care providers and recipients. We reviewed the literature to evaluate the types of publications on the subject, the specific dermatological topics addressed by AI, and the most challenging barriers to its implementation. A substantial number of original articles and commentaries have been published to date and only few detailed reviews exist. Most AI applications focus on differentiating between benign and malignant skin lesions, however; others exist pertaining to ulcers, inflammatory skin diseases, allergen exposure, dermatopathology, and gene expression profiling. Applications commonly analyze and classify images, however, other tools such as risk assessment calculators are becoming increasingly available. Although many applications are technologically feasible, important implementation barriers have been identified including systematic biases, difficulty of standardization, interpretability, and acceptance by physicians and patients alike. This review provides insight into future research needs and possibilities. There is a strong need for clinical investigation in dermatology providing evidence of success overcoming the identified barriers. With these research goals in mind, an appropriate role for AI in dermatology may be achieved in not so distant future.

Systematic review of machine learning for diagnosis and prognosis in dermatology

Background: Software systems using artificial intelligence for medical purposes have been developed in recent years. The success of deep neural networks (DNN) in 2012 in the image recognition challenge ImageNet LSVRC 2010 fueled expectations of the potential for using such systems in dermatology.Objective: To evaluate the ways in which machine learning has been utilized in dermatology to date and provide an overview of the findings in current literature on the subject.Methods: We conducted a systematic review of existing literature, identifying the literature through a systematic search of the PubMed database. Two doctors assessed screening and eligibility with respect to pre-determined inclusion and exclusion criteria.Results: A total of 2175 publications were identified, and 64 publications were included. We identified eight major categories where machine learning tools were tested in dermatology. Most systems involved image recognition tools that were primarily aimed at binary classification of malignant melanoma (MM). Short system descriptions and results of all included systems are presented in tables.Conclusions: We present a complete overview of artificial intelligence implemented in dermatology. Impressive outcomes were reported in all of the identified eight categories, but head-to-head comparison proved difficult. The many areas of dermatology where we identified machine learning tools indicate the diversity of machine learning.

Deep learning applications in ophthalmology

PURPOSE OF REVIEW

To describe the emerging applications of deep learning in ophthalmology.

RECENT FINDINGS

Recent studies have shown that various deep learning models are capable of detecting and diagnosing various diseases afflicting the posterior segment of the eye with high accuracy. Most of the initial studies have centered around detection of referable diabetic retinopathy, age-related macular degeneration, and glaucoma.

SUMMARY

Deep learning has shown promising results in automated image analysis of fundus photographs and optical coherence tomography images. Additional testing and research is required to clinically validate this technology.