Assessing outcomes of ear molding therapy by health care providers and convolutional neural network

Efficiency of ear molding for treating constricted ears of different severity

PURPOSE

To investigate the treatment time and efficiency of constricted ears of different severity after correction.

MATERIALS AND METHODS

We included the patients with constricted ear presented to our hospital for treatment between December 2021 and December 2023 in this retrospective analysis. The patients were divided into class I, II and III groups based on the severity of the constriction. Then we collected the data on classification of severity from each patient, together with sex, family history, age at initial correction, being informed upon diagnosis after birth, as well as utilization of auricle correction system. Logistic regression analysis was performed to identify the factors associated with the treatment time and efficiency.

RESULTS

The correction system yielded a high effective rate in the constricted ears. The treatment time in class II was significantly longer compared with those of class I after adjusting these parameters. Compared with the cases of class I, those with a class III showed significant attenuation in the symptoms and conditions (95 % CI: 0.034, 0.365; P < 0.001), after adjusting the age at initial correction, being informed upon diagnosis after birth, and utilization of auricle correction system. There were no statistical differences between class II and III in the treatment efficiency after correction.

CONCLUSIONS

The Amazing Ear Correction System was effective in treating constricted ear, yielding satisfactory treatment efficiency. Patients with class II constriction required longer treatment time compared with those of class I. The treatment outcome in the class I constriction was better than that of class III.

Ear Molding in Children-Timing, Technique, and Follow-up: A Systematic Review

Background: Nonsurgical management of congenital ear anomalies using molding devices shows efficacy but lacks standardization of treatment protocols and outcome measures. Learning Objective: To compare ear molding techniques and identify factors related to treatment outcomes. Design Type: Systematic review of the literature (1990-2021). Methods: Studies reporting molding for congenital ear anomalies were assessed. PRISMA guidelines were used. Data extracted included: age at treatment initiation, treatment duration, correction rates, and complications. Data analysis included descriptive statistics and outcomes were compared using the Student t-test. Results: In total, 37 studies with 3,341 patients (mean patients per study, 95; range, 5-488) were included. Infants in whom treatment was initiated at 4.8 weeks (median, 3.7; range, 0.9-8.8 weeks) were treated for 5.1 weeks (median 4.7, range 2.6-7.6 weeks) with 11.0 months follow-up (median 11.4, range 1.4-21.0 months). Individualized devices (physician-customized) were used more (62.2% of studies) than commercial devices. No difference in correction (p = 0.44) or complication rates (p = 0.19) was identified between devices. Totally, 70.3% of studies reported complications and 40.5% of studies included long-term follow-up data. Conclusions: The available evidence supports initiating ear molding in the first weeks of life to be most effective, yet outcome data should be standardized in future studies to improve evidence quality.

A publicly available newborn ear shape dataset for medical diagnosis of auricular deformities

Early and accurate diagnosis of ear deformities in newborns is crucial for an effective non-surgical correction treatment, since this commonly seen ear anomalies would affect aesthetics and cause mental problems if untreated. It is not easy even for experienced physicians to diagnose the auricular deformities of newborns and the classification of the sub-types, because of the rich bio-metric features embedded in the ear shape. Machine learning has already been introduced to analyze the auricular shape. However, there is little publicly available datasets of ear images from newborns. We released a dataset that contains quality-controlled photos of 3,852 ears from 1,926 newborns. The dataset also contains medical diagnosis of the ear shape, and the health data of each newborn and its mother. Our aim is to provide a freely accessible dataset, which would facilitate researches related with ear anatomies, such as the AI-aided detection and classification of auricular deformities and medical risk analysis.

A Narrative Review of Artificial Intelligence (AI) for Objective Assessment of Aesthetic Endpoints in Plastic Surgery

Notoriously characterized by subjectivity and lack of solid scientific validation, reporting aesthetic outcome in plastic surgery is usually based on ill-defined end points and subjective measures very often from the patients' and/or providers' perspective. With the tremendous increase in demand for all types of aesthetic procedures, there is an urgent need for better understanding of aesthetics and beauty in addition to reliable and objective outcome measures to quantitate what is perceived as beautiful and attractive. In an era of evidence-based medicine, recognition of the importance of science with evidence-based approach to aesthetic surgery is long overdue. View the many limitations of conventional outcome evaluation tools of aesthetic interventions, objective outcome analysis provided by tools described to be reliable is being investigated such as advanced artificial intelligence (AI). The current review is intended to analyze available evidence regarding advantages as well as limitations of this technology in objectively documenting outcome of aesthetic interventions. It has shown that some AI applications such as facial emotions recognition systems are capable of objectively measuring and quantitating patients' reported outcomes and defining aesthetic interventions success from the patients' perspective. Though not reported yet, observers' satisfaction with the results and their appreciation of aesthetic attributes may also be measured in the same manner.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 .

Applications of Machine Learning in Facial Cosmetic Surgeries: A Scoping Review

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 .

Artificial intelligence for assessing the severity of microtia via deep convolutional neural networks

Background

Microtia is a congenital abnormality varying from slightly structural abnormalities to the complete absence of the external ear. However, there is no gold standard for assessing the severity of microtia.

Objectives

The purpose of this study was to develop and test models of artificial intelligence to assess the severity of microtia using clinical photographs.

Methods

A total of 800 ear images were included, and randomly divided into training, validation, and test set. Nine convolutional neural networks (CNNs) were trained for classifying the severity of microtia. The evaluation metrics, including accuracy, precision, recall, F1 score, receiver operating characteristic curve, and area under the curve (AUC) values, were used to evaluate the performance of the models.

Results

Eight CNNs were tested with accuracy greater than 0.8. Among them, Alexnet and Mobilenet achieved the highest accuracy of 0.9. Except for Mnasnet, all CNNs achieved high AUC values higher than 0.9 for each grade of microtia. In most CNNs, the grade I microtia had the lowest AUC values and the normal ear had the highest AUC values.

Conclusion

CNN can classify the severity of microtia with high accuracy. Artificial intelligence is expected to provide an objective, automated assessment of the severity of microtia.

A novel method to accurately locate the reconstructed auricle

BACKGROUND

Congenital microtia is a common congenital disease in children, the cause of which is still unclear. At present, the main treatment for congenital microtia is ear reconstruction. Accurately locating of the reconstructed ear on the affected side before ear reconstruction surgery is difficult, while it is the key of successful operation. Our ear reconstruction team has developed a novel method to accurately locate the reconstructed auricle. This novel method has achieved good results in clinical practice.

METHODS

Thirty patients with unilateral ear reconstruction, who underwent auricle reconstruction using our invented auricle reconstruction positioning method in the Plastic Surgery Hospital of Chinese Academy of Medical Sciences from January 2020 to July 2021, were enrolled in this study.

RESULTS

Through Wilcoxon signed rank test, we found that there was no statistical difference between the mean distance from the highest point of the patient's normal ear to the central axis of the nose and that from the highest point of the reconstructed ear to the central axis of the nose (P>0.05). Meanwhile, there was no statistical difference between the mean distance from the lowest point of the patient's normal ear to the central axis of the nose and that from the lowest point of the reconstructed ear to the central axis of the nose (P>0.05). The satisfaction rate of patients and their families to the location of the reconstructed auricle was 100%.

CONCLUSIONS

The novel method of locating the reconstructed auricle employs simple materials. The implementation process is easy, and the effect is significant. To a certain extent, it solves the difficulty of locating the reconstructed auricle in ear reconstruction operation. Although this method can only be applied to patients with unilateral microtia, we recommend it for locating the reconstructed auricle by every plastic surgeon.