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Espinosa Reyes JA, Puerta Romero M, Cobo R, Heredia N, Solís Ruiz LA, Corredor Zuluaga DA. Artificial Intelligence in Facial Plastic and Reconstructive Surgery: A Systematic Review. Facial Plast Surg 2024; 40:615-622. [PMID: 37992752 DOI: 10.1055/a-2216-5099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2023] Open
Abstract
Artificial intelligence (AI) is a technology that is evolving rapidly and is changing the world and medicine as we know it. After reviewing the PROSPERO database of systematic reviews, there is no article related to this topic in facial plastic and reconstructive surgery. The objective of this article was to review the literature regarding AI applications in facial plastic and reconstructive surgery.A systematic review of the literature about AI in facial plastic and reconstructive surgery using the following keywords: Artificial Intelligence, robotics, plastic surgery procedures, and surgery plastic and the following databases: PubMed, SCOPUS, Embase, BVS, and LILACS. The inclusion criteria were articles about AI in facial plastic and reconstructive surgery. Articles written in a language other than English and Spanish were excluded. In total, 17 articles about AI in facial plastic met the inclusion criteria; after eliminating the duplicated papers and applying the exclusion criteria, these articles were reviewed thoroughly. The leading type of AI used in these articles was computer vision, explicitly using models of convolutional neural networks to objectively compare the preoperative with the postoperative state in multiple interventions such as facial lifting and facial transgender surgery.In conclusion, AI is a rapidly evolving technology, and it could significantly impact the treatment of patients in facial plastic and reconstructive surgery. Legislation and regulations are developing slower than this technology. It is imperative to learn about this topic as soon as possible and that all stakeholders proactively promote discussions about ethical and regulatory dilemmas.
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Affiliation(s)
- Jorge Alberto Espinosa Reyes
- Department of Otolaryngology and Facial Plastic & Reconstructive Surgery, The Face & Nose Institute, Private Practice Clínica INO, Bogotá, DC, Colombia
| | - Mauricio Puerta Romero
- Department of Otolaryngology and Facial Plastic & Reconstructive Surgery, Private Practice Clínica Sebastían de Belalcázar, Cali, Valle del Cauca, Colombia
| | - Roxana Cobo
- Department of Otolaryngology and Facial Plastic & Reconstructive Surgery, The Face & Nose Institute, Private Practice at Clínica Imbanaco, Cali, Valle del Cauca Colombia
| | - Nicolas Heredia
- Department of Otolaryngology and Facial Plastic & Reconstructive Surgery, The Face & Nose Institute, Bogotá, DC, Colombia
| | - Luis Alberto Solís Ruiz
- Department of Otolaryngology and Facial Plastic & Reconstructive Surgery, Private Practice, Chihuahua, Chihuahua, México
| | - Diego Andres Corredor Zuluaga
- Department of Otolaryngology and Facial Plastic & Reconstructive Surgery, Private Practice, Pereira, Risaralda, Colombia
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Park KW, Diop M, Willens SH, Pepper JP. Artificial Intelligence in Facial Plastics and Reconstructive Surgery. Otolaryngol Clin North Am 2024; 57:843-852. [PMID: 38971626 DOI: 10.1016/j.otc.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/08/2024]
Abstract
Artificial intelligence (AI), particularly computer vision and large language models, will impact facial plastic and reconstructive surgery (FPRS) by enhancing diagnostic accuracy, refining surgical planning, and improving post-operative evaluations. These advancements can address subjective limitations of aesthetic surgery by providing objective tools for patient evaluation. Despite these advancements, AI in FPRS has yet to be fully integrated in the clinic setting and faces numerous challenges including algorithmic bias, ethical considerations, and need for validation. This article discusses current and emerging AI technologies in FPRS for the clinic setting, providing a glimpse of its future potential.
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Affiliation(s)
- Ki Wan Park
- Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, 801 Welch Road, Palo Alto, CA 94305, USA
| | - Mohamed Diop
- Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, 801 Welch Road, Palo Alto, CA 94305, USA
| | - Sierra Hewett Willens
- Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, 801 Welch Road, Palo Alto, CA 94305, USA
| | - Jon-Paul Pepper
- Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, 801 Welch Road, Palo Alto, CA 94305, USA.
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Shiraishi M, Tsuruda S, Tomioka Y, Chang J, Hori A, Ishii S, Fujinaka R, Ando T, Ohba J, Okazaki M. Advancement of Generative Pre-trained Transformer Chatbots in Answering Clinical Questions in the Practical Rhinoplasty Guideline. Aesthetic Plast Surg 2024:10.1007/s00266-024-04377-4. [PMID: 39322837 DOI: 10.1007/s00266-024-04377-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 09/03/2024] [Indexed: 09/27/2024]
Abstract
BACKGROUND The Generative Pre-trained Transformer (GPT) series, which includes ChatGPT, is an artificial large language model that provides human-like text dialogue. This study aimed to evaluate the performance of artificial intelligence chatbots in answering clinical questions based on practical rhinoplasty guidelines. METHODS Clinical questions (CQs) developed from the guidelines were used as question sources. For each question, we asked GPT-4 and GPT-3.5 (ChatGPT), developed by OpenAI, to provide answers for the CQs, Policy Level, Aggregate Evidence Quality, Level of Confidence in Evidence, and References. We compared the performance of the two types of artificial intelligence (AI) chatbots. RESULTS A total of 10 questions were included in the final analysis, and the AI chatbots correctly answered 90.0% of these. GPT-4 demonstrated a lower accuracy rate than GPT-3.5 in answering CQs, although without statistically significant difference (86.0% vs. 94.0%; p = 0.05), whereas GPT-4 showed significantly higher accuracy for the level of confidence in Evidence than GPT-3.5 (52.0% vs. 28.0%; p < 0.01). No statistical differences were observed in Policy Level, Aggregate Evidence Quality, and Reference Match. In addition, GPT-4 rated significantly higher in presenting existing references than GPT-3.5 (36.9% vs. 24.1%; p = 0.01). CONCLUSIONS The overall performance of GPT-4 was similar to that of GPT-3.5. However, GPT-4 provided existing references at a higher rate than GPT-3.5. GPT-4 has the potential to provide a more accurate reference in professional fields, including rhinoplasty. LEVEL OF EVIDENCE V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
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Affiliation(s)
- Makoto Shiraishi
- Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
| | - Saori Tsuruda
- Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Yoko Tomioka
- Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Jinwoo Chang
- Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Asei Hori
- Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Saaya Ishii
- Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Rei Fujinaka
- Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Taku Ando
- Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Jun Ohba
- Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Mutsumi Okazaki
- Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
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Fung E, Patel D, Tatum S. Artificial intelligence in maxillofacial and facial plastic and reconstructive surgery. Curr Opin Otolaryngol Head Neck Surg 2024; 32:257-262. [PMID: 38837245 DOI: 10.1097/moo.0000000000000983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
PURPOSE OF REVIEW To provide a current review of artificial intelligence and its subtypes in maxillofacial and facial plastic surgery including a discussion of implications and ethical concerns. RECENT FINDINGS Artificial intelligence has gained popularity in recent years due to technological advancements. The current literature has begun to explore the use of artificial intelligence in various medical fields, but there is limited contribution to maxillofacial and facial plastic surgery due to the wide variance in anatomical facial features as well as subjective influences. In this review article, we found artificial intelligence's roles, so far, are to automatically update patient records, produce 3D models for preoperative planning, perform cephalometric analyses, and provide diagnostic evaluation of oropharyngeal malignancies. SUMMARY Artificial intelligence has solidified a role in maxillofacial and facial plastic surgery within the past few years. As high-quality databases expand with more patients, the role for artificial intelligence to assist in more complicated and unique cases becomes apparent. Despite its potential, ethical questions have been raised that should be noted as artificial intelligence continues to thrive. These questions include concerns such as compromise of the physician-patient relationship and healthcare justice.
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Affiliation(s)
| | | | - Sherard Tatum
- Department of Otolaryngology
- Department of Pediatrics, SUNY Upstate Medical University, Syracuse, New York, USA
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Czako L, Sufliarsky B, Simko K, Sovis M, Vidova I, Farska J, Lifková M, Hamar T, Galis B. Exploring the Practical Applications of Artificial Intelligence, Deep Learning, and Machine Learning in Maxillofacial Surgery: A Comprehensive Analysis of Published Works. Bioengineering (Basel) 2024; 11:679. [PMID: 39061761 PMCID: PMC11274331 DOI: 10.3390/bioengineering11070679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Revised: 05/29/2024] [Accepted: 06/13/2024] [Indexed: 07/28/2024] Open
Abstract
Artificial intelligence (AI), deep learning (DL), and machine learning (ML) are computer, machine, and engineering systems that mimic human intelligence to devise procedures. These technologies also provide opportunities to advance diagnostics and planning in human medicine and dentistry. The purpose of this literature review was to ascertain the applicability and significance of AI and to highlight its uses in maxillofacial surgery. Our primary inclusion criterion was an original paper written in English focusing on the use of AI, DL, or ML in maxillofacial surgery. The sources were PubMed, Scopus, and Web of Science, and the queries were made on the 31 December 2023. The search strings used were "artificial intelligence maxillofacial surgery", "machine learning maxillofacial surgery", and "deep learning maxillofacial surgery". Following the removal of duplicates, the remaining search results were screened by three independent operators to minimize the risk of bias. A total of 324 publications from 1992 to 2023 were finally selected. These were calculated according to the year of publication with a continuous increase (excluding 2012 and 2013) and R2 = 0.9295. Generally, in orthognathic dentistry and maxillofacial surgery, AI and ML have gained popularity over the past few decades. When we included the keywords "planning in maxillofacial surgery" and "planning in orthognathic surgery", the number significantly increased to 7535 publications. The first publication appeared in 1965, with an increasing trend (excluding 2014-2018), with an R2 value of 0.8642. These technologies have been found to be useful in diagnosis and treatment planning in head and neck surgical oncology, cosmetic and aesthetic surgery, and oral pathology. In orthognathic surgery, they have been utilized for diagnosis, treatment planning, assessment of treatment needs, and cephalometric analyses, among other applications. This review confirms that the current use of AI and ML in maxillofacial surgery is focused mainly on evaluating digital diagnostic methods, especially radiology, treatment plans, and postoperative results. However, as these technologies become integrated into maxillofacial surgery and robotic surgery in the head and neck region, it is expected that they will be gradually utilized to plan and comprehensively evaluate the success of maxillofacial surgeries.
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Affiliation(s)
- Ladislav Czako
- Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava and University Hospital, Ruzinovska 6, 826 06 Bratislava, Slovakia; (L.C.); (K.S.); (M.S.); (I.V.); (J.F.); (B.G.)
| | - Barbora Sufliarsky
- Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava and University Hospital, Ruzinovska 6, 826 06 Bratislava, Slovakia; (L.C.); (K.S.); (M.S.); (I.V.); (J.F.); (B.G.)
| | - Kristian Simko
- Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava and University Hospital, Ruzinovska 6, 826 06 Bratislava, Slovakia; (L.C.); (K.S.); (M.S.); (I.V.); (J.F.); (B.G.)
| | - Marek Sovis
- Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava and University Hospital, Ruzinovska 6, 826 06 Bratislava, Slovakia; (L.C.); (K.S.); (M.S.); (I.V.); (J.F.); (B.G.)
| | - Ivana Vidova
- Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava and University Hospital, Ruzinovska 6, 826 06 Bratislava, Slovakia; (L.C.); (K.S.); (M.S.); (I.V.); (J.F.); (B.G.)
| | - Julia Farska
- Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava and University Hospital, Ruzinovska 6, 826 06 Bratislava, Slovakia; (L.C.); (K.S.); (M.S.); (I.V.); (J.F.); (B.G.)
| | - Michaela Lifková
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, St. Elisabeth Hospital Bratislava, Heydukova 10, 812 50 Bratislava, Slovakia;
| | - Tomas Hamar
- Institute of Medical Terminology and Foreign Languages, Faculty of Medicine, Comenius University in Bratislava, Moskovska 2, 811 08 Bratislava, Slovakia;
| | - Branislav Galis
- Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava and University Hospital, Ruzinovska 6, 826 06 Bratislava, Slovakia; (L.C.); (K.S.); (M.S.); (I.V.); (J.F.); (B.G.)
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Saito T, Lo CC, Tu JCY, Hattori Y, Chou PY, Lo LJ. Secondary Bilateral Cleft Rhinoplasty: Achieving an Aesthetic Result. Aesthet Surg J 2024; 44:NP365-NP378. [PMID: 38314894 DOI: 10.1093/asj/sjae019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 01/17/2024] [Accepted: 01/22/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND Secondary rhinoplasty in patients with bilateral cleft lip poses ongoing challenges and requires a reliable method for achieving optimal outcomes. OBJECTIVES The purpose of this study was to establish a safe and effective method for secondary bilateral cleft rhinoplasty. METHODS A consecutive series of 92 skeletally matured patients with bilateral cleft lip and nasal deformity were included. All had undergone secondary open rhinoplasty, performed by a single surgeon with a bilateral reverse-U flap and septal extension graft, between 2013 and 2021. Medical records of these 92 patients were reviewed to assess the clinical course. A 3-dimensional (3D) anthropometric analysis and panel assessment of 32 patients were performed to evaluate the aesthetic improvement, with an age-, sex-, and ethnicity-matched normal control group for comparisons. RESULTS The methods showed statistically significant improvement in addressing a short columella (columellar height), short nasal bridge (nasal bridge length), de-projected nasal tip (nasal tip projection, nasal dorsum angle), poorly defined nasal tip (nasal tip angle, dome height, and panel assessment), and transversely oriented nostrils (columellar height, alar width, nostril type). Importantly, these improvements were accompanied by a low complication rate of 4%. However, upper lip deficiency over the upper lip angle and labial-columellar angle remained without significant improvement. CONCLUSIONS In this study we described effective secondary rhinoplasty, which was composed of a bilateral reverse-U flap and septal extension graft, with acceptable outcome. The 3D anthropometric analysis and panel assessment clarified that our rhinoplasty procedure could bring the nasal morphology in these patients closer to the normal data. LEVEL OF EVIDENCE: 3
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Li R, Shu F, Zhen Y, Song Z, An Y, Jiang Y. Artificial Intelligence for Rhinoplasty Design in Asian Patients. Aesthetic Plast Surg 2024; 48:1557-1564. [PMID: 37580565 DOI: 10.1007/s00266-023-03534-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 07/17/2023] [Indexed: 08/16/2023]
Abstract
BACKGROUND Rhinoplasty is one of the most challenging plastic surgeries because it lacks a uniform standard for preoperative design or implementation. For a long time, rhinoplasties were done without an accurate consensus of aesthetic design between surgeons and patients before surgery and consequently brought unsatisfactory appearance for patients. In recent years, three-dimensional (3D) simulation has been used to visualize the preoperative design of rhinoplasty, and good results have been achieved. However, it still relied on individual aesthetics and experience. The preoperative design remained a huge challenge for inexperienced surgeons and could be time-consuming to perform manually. Therefore, we adopted artificial intelligence (AI) in this work to provide a new idea for automated and efficient preoperative nasal contour design. METHODS We collected a dataset of 3D facial images from 209 patients. For each patient, both the original face and the manually designed face using 3D simulation software were included. The 3D images were transformed into point clouds, based on which we used the modified FoldingNet model for deep neural network training (by pytorch 1.12). RESULTS The trained AI model gained the ability to perform aesthetic design automatically and achieved similar results to manual design. We analysed the 1027 facial features captured by the AI model and concluded two of its possible cognitive modes. One is to resemble the human aesthetic considerations while the other is to fulfil the given task in a special way of the machine. CONCLUSION We presented the first AI model for automated preoperative 3D simulation of rhinoplasty in this study. It provided a new idea for the automated, individual and efficient preoperative design, which was expected to bring a new paradigm for rhinoplasty and even the whole field of plastic surgery. LEVEL OF EVIDENCE IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
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Affiliation(s)
- Ruoyu Li
- Department of Plastic Surgery, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China
| | - Fan Shu
- Department of Plastic Surgery, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China
| | - Yonghuan Zhen
- Department of Plastic Surgery, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China
| | - Zhexiang Song
- Department of Physics, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100191, China
| | - Yang An
- Department of Plastic Surgery, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China.
| | - Yin Jiang
- Department of Physics, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100191, China.
- Beihang Hangzhou Innovation Institute, Yuhang, Hangzhou, 310023, China.
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Ashoori M, Zoroofi RA, Sadeghi M. An Automatic Framework for Nasal Esthetic Assessment by ResNet Convolutional Neural Network. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:455-470. [PMID: 38343266 PMCID: PMC11031543 DOI: 10.1007/s10278-024-00973-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 11/30/2023] [Accepted: 12/04/2023] [Indexed: 04/20/2024]
Abstract
Nasal base aesthetics is an interesting and challenging issue that attracts the attention of researchers in recent years. With that insight, in this study, we propose a novel automatic framework (AF) for evaluating the nasal base which can be useful to improve the symmetry in rhinoplasty and reconstruction. The introduced AF includes a hybrid model for nasal base landmarks recognition and a combined model for predicting nasal base symmetry. The proposed state-of-the-art nasal base landmark detection model is trained on the nasal base images for comprehensive qualitative and quantitative assessments. Then, the deep convolutional neural networks (CNN) and multi-layer perceptron neural network (MLP) models are integrated by concatenating their last hidden layer to evaluate the nasal base symmetry based on geometry features and tiled images of the nasal base. This study explores the concept of data augmentation by applying the methods motivated via commonly used image augmentation techniques. According to the experimental findings, the results of the AF are closely related to the otolaryngologists' ratings and are useful for preoperative planning, intraoperative decision-making, and postoperative assessment. Furthermore, the visualization indicates that the proposed AF is capable of predicting the nasal base symmetry and capturing asymmetry areas to facilitate semantic predictions. The codes are accessible at https://github.com/AshooriMaryam/Nasal-Aesthetic-Assessment-Deep-learning .
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Affiliation(s)
- Maryam Ashoori
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Reza A Zoroofi
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Mohammad Sadeghi
- Tehran University of Medical Sciences, Imam Khomeini Hospital Complex, Tehran, Iran
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Gao H, Shen G, Hu H, Lin Z, Yuan H, Lin D, Zhu X, Jiang H, Liu A. Sutures positioning technique enhances the predictability and concordance between preoperative simulation and actual outcomes in rhinoplasty. J Plast Reconstr Aesthet Surg 2023; 86:72-78. [PMID: 37716252 DOI: 10.1016/j.bjps.2023.08.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 07/09/2023] [Accepted: 08/13/2023] [Indexed: 09/18/2023]
Abstract
BACKGROUND The predictability and concordance between simulated and actual outcomes in rhinoplasty are uncertain. Here, we introduce a suture positioning technique (SPT), a simple and low-cost method to minimize the gap between the simulated and actual outcomes of rhinoplasty. METHODS Seventy patients were enrolled in this study between January 2018 and January 2021. Preoperative simulations were performed using Adobe Photoshop. The control group underwent surgery using simulation and intuition. In the SPT group, sutures were used to assist in the preoperative identification of the ideal nasal tip position. The SPT effectiveness was tested by measuring the nasal parameters and using the patient's subjective satisfaction questionnaire at T1 (Time 1, immediately postoperatively) and T2 (Time 2, at least 1 year postoperatively). RESULTS The intraclass correlation coefficient test showed a satisfactory correlation between simulation and postoperative outcomes in both groups. However, the SPT group had a higher correlation than the control group, especially for the nasal length (16% higher at T1 and 15% higher at T2). The mean absolute difference (MAD) between the outcomes and simulation indicated that the MAD of nasal tip projection between T2 and simulation and MAD of nasal length between T1 (or T2) and simulation were statistically significant between groups. Additionally, the SPT group was more satisfied with the postoperative outcomes and were consistent with the preoperative simulation. CONCLUSION This study demonstrated the effectiveness of SPT in intraoperative quality control. This technique may be adopted by surgeons to achieve good concordance between simulated and actual surgical outcomes.
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Affiliation(s)
- Hong Gao
- Department of Plastic and Reconstructive Surgery, Second Affiliated Hospital (Chang Zheng Hospital) of Naval Medical University, 415 Fengyang Road, Huangpu District, Shanghai 200003, China
| | - Gan Shen
- Department of Plastic and Reconstructive Surgery, Second Affiliated Hospital (Chang Zheng Hospital) of Naval Medical University, 415 Fengyang Road, Huangpu District, Shanghai 200003, China
| | - Hao Hu
- Department of Plastic Surgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China
| | - Zhangxiang Lin
- Department of Plastic and Reconstructive Surgery, Second Affiliated Hospital (Chang Zheng Hospital) of Naval Medical University, 415 Fengyang Road, Huangpu District, Shanghai 200003, China
| | - Hanli Yuan
- Department of Plastic and Reconstructive Surgery, Second Affiliated Hospital (Chang Zheng Hospital) of Naval Medical University, 415 Fengyang Road, Huangpu District, Shanghai 200003, China
| | - Defeng Lin
- Department of Plastic and Reconstructive Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaohai Zhu
- Department of Plastic and Reconstructive Surgery, Second Affiliated Hospital (Chang Zheng Hospital) of Naval Medical University, 415 Fengyang Road, Huangpu District, Shanghai 200003, China.
| | - Hua Jiang
- Department of Plastic Surgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China.
| | - Antang Liu
- Department of Plastic and Reconstructive Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Seth I, Lim B, Xie Y, Cevik J, Rozen WM, Ross RJ, Lee M. Comparing the Efficacy of Large Language Models ChatGPT, BARD, and Bing AI in Providing Information on Rhinoplasty: An Observational Study. Aesthet Surg J Open Forum 2023; 5:ojad084. [PMID: 37795257 PMCID: PMC10547367 DOI: 10.1093/asjof/ojad084] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2023] Open
Abstract
Background Large language models (LLMs) are emerging artificial intelligence (AI) technologies refining research and healthcare. However, the impact of these models on presurgical planning and education remains under-explored. Objectives This study aims to assess 3 prominent LLMs-Google's AI BARD (Mountain View, CA), Bing AI (Microsoft, Redmond, WA), and ChatGPT-3.5 (Open AI, San Francisco, CA) in providing safe medical information for rhinoplasty. Methods Six questions regarding rhinoplasty were prompted to ChatGPT, BARD, and Bing AI. A Likert scale was used to evaluate these responses by a panel of Specialist Plastic and Reconstructive Surgeons with extensive experience in rhinoplasty. To measure reliability, the Flesch Reading Ease Score, the Flesch-Kincaid Grade Level, and the Coleman-Liau Index were used. The modified DISCERN score was chosen as the criterion for assessing suitability and reliability. A t test was performed to calculate the difference between the LLMs, and a double-sided P-value <.05 was considered statistically significant. Results In terms of reliability, BARD and ChatGPT demonstrated a significantly (P < .05) greater Flesch Reading Ease Score of 47.47 (±15.32) and 37.68 (±12.96), Flesch-Kincaid Grade Level of 9.7 (±3.12) and 10.15 (±1.84), and a Coleman-Liau Index of 10.83 (±2.14) and 12.17 (±1.17) than Bing AI. In terms of suitability, BARD (46.3 ± 2.8) demonstrated a significantly greater DISCERN score than ChatGPT and Bing AI. In terms of Likert score, ChatGPT and BARD demonstrated similar scores and were greater than Bing AI. Conclusions BARD delivered the most succinct and comprehensible information, followed by ChatGPT and Bing AI. Although these models demonstrate potential, challenges regarding their depth and specificity remain. Therefore, future research should aim to augment LLM performance through the integration of specialized databases and expert knowledge, while also refining their algorithms. Level of Evidence 5
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Affiliation(s)
- Ishith Seth
- Corresponding Author: Dr Ishith Seth, Faculty of Medicine, Monash University, Melbourne, Victoria 3004, Australia. E-mail:
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Ahmadi N, Niazmand M, Ghasemi A, Mohaghegh S, Motamedian SR. Applications of Machine Learning in Facial Cosmetic Surgeries: A Scoping Review. Aesthetic Plast Surg 2023; 47:1377-1393. [PMID: 37277660 DOI: 10.1007/s00266-023-03379-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 04/23/2023] [Indexed: 06/07/2023]
Abstract
OBJECTIVE To review the application of machine learning (ML) in the facial cosmetic surgeries and procedures METHODS AND MATERIALS: Electronic search was conducted in PubMed, Scopus, Embase, Web of Science, ArXiv and Cochrane databases for the studies published until August 2022. Studies that reported the application of ML in various fields of facial cosmetic surgeries were included. The studies' risk of bias (ROB) was assessed using the QUADAS-2 tool and NIH tool for before and after studies. RESULTS From 848 studies, a total of 29 studies were included and categorized in five groups based on the aim of the studies: outcome evaluation (n = 8), face recognition (n = 7), outcome prediction (n = 7), patient concern evaluation (n = 4) and diagnosis (n = 3). Total of 16 studies used public data sets. ROB assessment using QUADAS-2 tool revealed that six studies were at low ROB, five studies were at high ROB, and others had moderate ROB. All studies assessed with NIH tool showed fair quality. In general, all studies showed that using ML in the facial cosmetic surgeries is accurate enough to benefit both surgeons and patients. CONCLUSION Using ML in the field of facial cosmetic surgery is a novel method and needs further studies, especially in the fields of diagnosis and treatment planning. Due to the small number of articles and the qualitative analysis conducted, we cannot draw a general conclusion about the impact of ML in the sphere of facial cosmetic surgery. LEVEL OF EVIDENCE IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
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Affiliation(s)
- Nima Ahmadi
- Student research committee, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, 1983963113, Iran
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maral Niazmand
- Student research committee, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, 1983963113, Iran
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Ghasemi
- Student research committee, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, 1983963113, Iran
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sadra Mohaghegh
- Student research committee, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, 1983963113, Iran
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Saeed Reza Motamedian
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, 1983963113, Iran.
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Cox A, Seth I, Xie Y, Hunter-Smith DJ, Rozen WM. Utilizing ChatGPT-4 for Providing Medical Information on Blepharoplasties to Patients. Aesthet Surg J 2023; 43:NP658-NP662. [PMID: 37032521 DOI: 10.1093/asj/sjad096] [Citation(s) in RCA: 34] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/05/2023] [Accepted: 04/05/2023] [Indexed: 04/11/2023] Open
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Rokhshad R, Keyhan SO, Yousefi P. Artificial intelligence applications and ethical challenges in oral and maxillo-facial cosmetic surgery: a narrative review. Maxillofac Plast Reconstr Surg 2023; 45:14. [PMID: 36913002 PMCID: PMC10011265 DOI: 10.1186/s40902-023-00382-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 03/01/2023] [Indexed: 03/14/2023] Open
Abstract
Artificial intelligence (AI) refers to using technologies to simulate human cognition to solve a specific problem. The rapid development of AI in the health sector has been attributed to the improvement of computing speed, exponential increase in data production, and routine data collection. In this paper, we review the current applications of AI for oral and maxillofacial (OMF) cosmetic surgery to provide surgeons with the fundamental technical elements needed to understand its potential. AI plays an increasingly important role in OMF cosmetic surgery in various settings, and its usage may raise ethical issues. In addition to machine learning algorithms (a subtype of AI), convolutional neural networks (a subtype of deep learning) are widely used in OMF cosmetic surgeries. Depending on their complexity, these networks can extract and process the elementary characteristics of an image. They are, therefore, commonly used in the diagnostic process for medical images and facial photos. AI algorithms have been used to assist surgeons with diagnosis, therapeutic decisions, preoperative planning, and outcome prediction and evaluation. AI algorithms complement human skills while minimizing shortcomings through their capabilities to learn, classify, predict, and detect. This algorithm should, however, be rigorously evaluated clinically, and a systematic ethical reflection should be conducted regarding data protection, diversity, and transparency. It is possible to revolutionize the practice of functional and aesthetic surgeries with 3D simulation models and AI models. Planning, decision-making, and evaluation during and after surgery can be improved with simulation systems. A surgical AI model can also perform time-consuming or challenging tasks for surgeons.
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Affiliation(s)
- Rata Rokhshad
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany. .,Department of Medicine, Boston University Medical Center, Boston, MA, USA.
| | - Seied Omid Keyhan
- College of Dentistry, Department of Oral & Maxillofacial Surgery, Gangneung-Wonju National University, Gangneung, South Korea.,Department of Oral & Maxillofacial Surgery, University of Florida, College of Medicine, Jacksonville, FL, USA.,Maxillofacial Surgery & Implantology & Biomaterial Research Foundation, Tehran, Iran.,Iface Academy, Atlanta, GA, USA
| | - Parisa Yousefi
- Maxillofacial Surgery & Implantology & Biomaterial Research Foundation, Tehran, Iran
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Eldaly AS, Avila FR, Torres-Guzman RA, Maita K, Garcia JP, Palmieri Serrano L, Forte AJ. Simulation and Artificial Intelligence in Rhinoplasty: A Systematic Review. Aesthetic Plast Surg 2022; 46:2368-2377. [PMID: 35437664 DOI: 10.1007/s00266-022-02883-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 03/19/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND Rhinoplasty is one of the most popular cosmetic procedures. The complexity of the nasal structure and the substantial aesthetic and functional impact of the operation make rhinoplasty very challenging. The past few years have witnessed an increasing implementation of artificial intelligence (AI) and simulation systems into plastic surgery practice. This review explores the potential uses of AI and simulation models in rhinoplasty. METHODS Five electronic databases were searched: PubMed, CINAHL, EMBASE, Scopus, and Web of Science. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis as our basis of organization. RESULTS Several simulation models were described to predict the nasal shape that aesthetically matches the patient's face, indicate the implant size in augmentation rhinoplasty and construct three-dimensional (3D) facial images from two-dimensional images. Machine learning was used to learn surgeons' rhinoplasty styles and accurately simulate the outcomes. Deep learning was used to predict rhinoplasty status accurately and analyze the factors associated with increased facial attractiveness after rhinoplasty. Finally, a deep learning model was used to predict patients' age before and after rhinoplasty proving that the procedure made the patients look younger. CONCLUSION 3D simulation models and AI models can revolutionalize the practice of functional and aesthetic rhinoplasty. Simulation systems can be beneficial in preoperative planning, intra-operative decision making, and postoperative evaluation. In addition, AI models can be trained to carry out tasks that are either challenging or time-consuming for surgeons. LEVEL OF EVIDENCE III This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
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Affiliation(s)
- Abdullah S Eldaly
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | - Francisco R Avila
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | | | - Karla Maita
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | - John P Garcia
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | - Luiza Palmieri Serrano
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | - Antonio J Forte
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA.
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