A Systematic Review of Artificial Intelligence Applications in Plastic Surgery

Looking to the Future

Daisy L. Spoer, MS; Julianne M. Kiene, MS; Paige K. Dekker, MD; Samuel S. Huffman, BS; Kevin G. Kim, MD; Andrew I. Abadeer, MD, MEng; Kenneth L. Fan, MD

Disclosures

Plast Reconstr Surg Glob Open. 2022;10(12):e4608

Abstract and Introduction

Abstract

Background: Artificial intelligence (AI) is presently employed in several medical specialties, particularly those that rely on large quantities of standardized data. The integration of AI in surgical subspecialties is under preclinical investigation but is yet to be widely implemented. Plastic surgeons collect standardized data in various settings and could benefit from AI. This systematic review investigates the current clinical applications of AI in plastic and reconstructive surgery.

Methods: A comprehensive literature search of the Medline, EMBASE, Cochrane, and PubMed databases was conducted for AI studies with multiple search terms. Articles that progressed beyond the title and abstract screening were then subcategorized based on the plastic surgery subspecialty and AI application.

Results: The systematic search yielded a total of 1820 articles. Forty-four studies met inclusion criteria warranting further analysis. Subcategorization of articles by plastic surgery subspecialties revealed that most studies fell into aesthetic and breast surgery (27%), craniofacial surgery (23%), or microsurgery (14%). Analysis of the research study phase of included articles indicated that the current research is primarily in phase 0 (discovery and invention; 43.2%), phase 1 (technical performance and safety; 27.3%), or phase 2 (efficacy, quality improvement, and algorithm performance in a medical setting; 27.3%). Only one study demonstrated translation to clinical practice.

Conclusions: The potential of AI to optimize clinical efficiency is being investigated in every subfield of plastic surgery, but much of the research to date remains in the preclinical status. Future implementation of AI into everyday clinical practice will require collaborative efforts.

Introduction

Artificial intelligence (AI) is unified by the idea of a system that displays intelligent behavior. AI's robust automated computing power can reduce diagnostic errors, conserve resources, and increase efficiency.^[1–3] The most successful clinical applications of AI are seen in medical specialties that inherently collect standardized data, such as radiology, pathology, ophthalmology, and dermatology.^[1] Conversely, the decreased emphasis on standardized data collection in surgery may be limiting an associated development of clinical AI.^[4] Plastic surgery is uniquely reliant on visual diagnosis, prediction, and assessment of aesthetic outcomes. Such AI tasks are achievable and could enhance efficiency and accuracy in plastic surgery. However, the standardization of data and integration of AI in plastic surgery is not well characterized.

In this systematic review, we aim to investigate the current applications of AI in plastic surgery within eight subspecialties: aesthetic and breast surgery, general plastic and reconstructive surgery, craniofacial surgery, burn surgery, microsurgery, oral and maxillofacial surgery, wound care, and hand surgery. We further distinguish the AI implemented in each study by subfield (Table 1) and study phase to characterize current applications and future directions for integrating AI into clinical practice.^[2,4,5–18]

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Table 1. Definitions and Examples of AI Subfields
Subfield	Description	Examples
ML	Machines can recognize patterns in data and make predictions without explicit programing⁵	Supervised ML: machine uses training data (eg, EKG tracings) to make output predictions (eg, normal sinus rhythm), which the machine compares to a known correct output (eg, myocardial infarction) to modify its algorithm accordingly.^5–7 Supervised ML must be programed to recognize appropriate features of a given input and be fed immense amounts of data points with known comparable output values to build an algorithm that is both generalizable and accurate.^1,8	Unsupervised ML: makes inferences on unlabeled data to discover groups within the data (clustering), propose rules that describe large portions of the data (association), or generate novel data points independently (generative modeling).^5,6,9,10 For example, unsupervised ML has been used to identify patients at risk for dementia based on population surveys by clustering survey respondents into likelihood groups.¹¹
NLP	Infers meaning and sentiment from unstructured human language data, such as video, audio files, or text files.	NLP can be useful for patient visit documentation and large-scale EMR analyses. For example, NLP has been used to identify words and phrases in operative reports that may predict postoperative complications.¹²
CV	Machines analyze images and videos with the goal of mimicking human visual perception and reasoning.	Involves image analysis using geometric and physical statistical modeling and data-based ML approaches.
ANN	Modeled after biological nervous systems: computational units or "neurons" in hidden intermediary layer(s) form connections between each other as well as the input and output cell layers.¹³	DNNs consist of several layers of processing, which can independently extract features from complex inputs (images/speech/video) and arrive at conclusions without the need for explicit programming.¹⁴ Some examples of DNNs include CNNs and GAN.²	CNNs perform independent perceptual tasks (eg, interpreting medical scans, pathology slides, skin lesions, facial features) and predict probabilities of classification (eg, presence or absence of disease) beyond the scope which computer vision was previously capable of.¹⁵ CNN has shown validated clinical efficacy in the detection of diabetic retinopathy, wrist fractures, histologic breast metastases, and small colonic polyps.² GANs are composed of two networks which synergistically generate data samples.¹⁶ While the generator uses the data set (eg, real photos of malignant melanoma) to produce novel samples of data (eg, fake photographs of malignant melanoma), the discriminator trains the generator by distinguishing data as real or fake. This mechanism ultimately yields generated data which are indistinguishable from the original set.^11,6,17 GAN have been used to de-noise/reconstruct and synthesize medical images for patient privacy, cross modality imaging (MR↔ CT, histopathology color normalization, MR→PET, real ↔ synthetic), and training sets for CNNs or clinicians.^22,17,18

ANNs, artificial neural networks; CNNs, convolutional neural network; CV, computer vision; DNNs, deep neural networks; GANs, generative adversarial networks; ML, machine learning; NLP, natural language processing.

Table 2. Example Search Strategy Performed in Medline
Number	Searches
1	Plastic surgery.mp. or exp Surgery, Plastic/
2	Reconstructive surgical procedures.mp. or exp Reconstructive Surgical Procedures/
3	exp Esthetics/or Esthetics.mp.
4	exp Microsurgery/or microsurgery.mp.
5	1 or 2 or 3 or 4
6	artificial intelligence.mp. or exp Artificial Intelligence/
7	Algorithms.mp. or exp Algorithms/
8	Neural Networks Computer.mp. or exp "Neural Networks (Computer)"/
9	artificial neural network.mp.
10	Computer Simulation.mp. or exp Computer Simulation/
11	Machine Learning.mp. or exp Machine Learning/
12	Deep learning.mp. or exp Deep Learning/
13	Pattern Recognition, Automated.mp. or exp Pattern Recognition, Automated/
14	Three-Dimensional Imaging.mp. or exp Imaging, Three-Dimensional/
15	Automation.mp. or exp Automation/
16	Electronic Data Processing.mp. or exp Electronic Data Processing/
17	Information processing.mp. or exp Electronic Data Processing/
18	Computer-Aided Design.mp. or exp Computer-Aided Design/
19	Computer Security.mp. or exp Computer Security/
20	Computer Simulation.mp. or exp Computer Simulation/
21	Image Processing, Computer-Assisted.mp. or exp Image Processing, Computer-Assisted/
22	Signal Processing, Computer-Assisted.mp. or exp Signal Processing, Computer-Assisted/
23	Facial Recognition.mp. or exp Facial Recognition/
24	Biomedical Technology.mp. or exp Biomedical Technology/
25	Computer assisted diagnosis.mp. or exp Diagnosis, Computer-Assisted/
26	Robotics.mp. or exp Robotics/
27	Electronic Data Processing.mp. or exp Electronic Data Processing/
28	Information processing.mp. or exp Electronic Data Processing/
29	Big data.mp. or exp Big Data/
30	Radiographic Image Interpretation, Computer Assisted.mp. or exp Radiographic Image Interpretation, Computer-Assisted/
31	Computer assisted diagnosis.mp. or exp Diagnosis, Computer-Assisted/
32	Biometric Identification.mp. or exp Biometric Identification/
33	biometry.mp. or exp Biometry/
34	6 or 7 or 8 or 9 or 10 or 11 or 12 or 13 or 14 or 15 or 16 or 17 or 18 or 19 or 20 or 21 or 22 or 23 or 24 or 25 or 26 or 27 or 28 or 29 or 30 or 31 or 32 or 33
35	5 and 34
36	6 and 35
37	Limit 36 to English language