Smartphone Integration of Artificial Intelligence for Automated Plagiocephaly Diagnosis.

Positional plagiocephaly is a pediatric condition with important cosmetic implications affecting ∼40% of infants under 12 months of age. Early diagnosis and treatment initiation is imperative in achieving satisfactory outcomes; improved diagnostic modalities are needed to support this goal. This study aimed to determine whether a smartphone-based artificial intelligence tool could diagnose positional plagiocephaly. Methods: A prospective validation study was conducted at a large tertiary care center with two recruitment sites: (1) newborn nursery, (2) pediatric craniofacial surgery clinic. Eligible children were aged 0-12 months with no history of hydrocephalus, intracranial tumors, intracranial hemorrhage, intracranial hardware, or prior craniofacial surgery. Successful artificial intelligence diagnosis required identification of the presence and severity of positional plagiocephaly. Results: A total of 89 infants were prospectively enrolled from the craniofacial surgery clinic (n = 25, 17 male infants [68%], eight female infants [32%], mean age 8.44 months) and newborn nursery (n = 64, 29 male infants [45%], 25 female infants [39%], mean age 0 months). The model obtained a diagnostic accuracy of 85.39% compared with a standard clinical examination with a disease prevalence of 48%. Sensitivity was 87.50% [95% CI, 75.94-98.42] with a specificity of 83.67% [95% CI, 72.35-94.99]. Precision was 81.40%, while likelihood ratios (positive and negative) were 5.36 and 0.15, respectively. The F1-score was 84.34%. Conclusions: The smartphone-based artificial intelligence algorithm accurately diagnosed positional plagiocephaly in a clinical environment. This technology may provide value by helping guide specialist consultation and enabling longitudinal quantitative monitoring of cranial shape.

The Development of a Novel Bariatric Laparoscopic Simulator.

Bariatric surgery presents a specific challenge in surgical education; simulators need to take into account the specific technical difficulties related to the patient population but also to various types of surgery. We interviewed several leaders in the fields of bariatric and general surgery with experience in laparoscopic surgery and developed a bariatric-specific laparoscopic simulator. This novel simulator was constructed using a variety of silicone-based materials and 3D printing techniques to be reusable and adjustable for a variety of procedures, with no essential components being disposed of following each use. Expert surgeons (n=4) with knowledge on bariatric procedures were recruited and asked to perform a simple simulated laparoscopic procedure. Following testing, participants were asked to complete a survey and rate the simulator based on its physical attributes, global realism, usefulness in improving surgical skills, and overall surgical experience. Face and content validation outcomes based on the questionnaire evaluations completed by expert surgeons showed very good results, with an overall mean score of 4.3 out of 5 (86%). These preliminary results highlight the potential for the simulator's application as a tool to improve bariatric surgical education and patient outcomes.

Practical Review of the Cost of Diagnosis and Management of Positional Plagiocephaly.

Positional plagiocephaly has garnered increased research interest since the introduction of the Back to Sleep campaign in the 1990s, and the subsequent increase in infants with cranial deformity. Research has focused on treatment outcomes and developing new modalities to address asymmetric heads. Little attention has been given to the cost of treatment and diagnosis. This study aimed to summarize the literature and provide an overview of the costs associated with a diagnosis of positional plagiocephaly. Methods: A literature review was performed by searching PubMed and Ovid Embase to identify studies pertaining to the "cost" of plagiocephaly diagnosis or treatment through direct financial factors, disturbance to daily routines (ie, through treatment prolongation), or related stress. Results: Twenty-nine peer-reviewed studies were included. Treatment options for plagiocephaly are stratified by severity and age of diagnosis, with different pathways available to treat different stages of asymmetry. The common factor across all treatment modalities is that earlier diagnosis unequivocally leads to better aesthetic outcomes and shorter treatment times. This leads to lower costs for treatment, a lower stress burden for parents, and lower costs for the healthcare system in the future through reduction of long-term effects. Our theoretical cost model suggests that early diagnosis at 4 months can lead to a treatment cost of $1495, when compared with $5195 for detection of deformity at or after 6 months. Conclusion: The dramatic cost disparity between early and late diagnosis highlights the need for reliable methods to accurately detect cranial deformity early in an infant's life.

Novel Screening and Monitoring Techniques for Deformational Plagiocephaly: A Systematic Review.

This article summarizes the current state of diagnostic modalities for infant craniofacial deformities and highlights capable diagnostic tools available currently to pediatricians.

BreastGAN: Artificial Intelligence-Enabled Breast Augmentation Simulation.

BACKGROUND: Managing patient expectations is important to ensuring patient satisfaction in aesthetic medicine. To this end, computer technology developed to photograph, digitize, and manipulate three-dimensional (3D) objects has been applied to the female breast. However, the systems remain complex, physically cumbersome, and extremely expensive. OBJECTIVES: The authors of the current study wish to introduce the plastic surgery community to BreastGAN, a portable, artificial intelligence (AI)-equipped tool trained on real clinical images to simulate breast augmentation outcomes. METHODS: Charts of all patients who underwent bilateral breast augmentation performed by the senior author were retrieved and analyzed. Frontal before and after images were collected from each patient's chart, cropped in a standardized fashion, and used to train a neural network designed to manipulate before images to simulate a surgical result. AI-generated frontal after images were then compared with the real surgical results. RESULTS: Standardizing the evaluation of surgical results is a timeless challenge which persists in the context of AI-synthesized after images. In this study, AI-generated images were comparable to real surgical results. CONCLUSIONS: This study features a portable, cost-effective neural network trained on real clinical images and designed to simulate surgical results following bilateral breast augmentation. Tools trained on a larger dataset of standardized surgical image pairs will be the subject of future studies.