Artificial Intelligence in Facial Plastic Surgery: A Review of Current Applications, Future Applications, and Ethical Considerations.

From virtual chat assistants to self-driving cars, artificial intelligence (AI) is often heralded as the technology that has and will continue to transform this generation. Among widely adopted applications in other industries, its potential use in medicine is being increasingly explored, where the vast amounts of data present in electronic health records and need for continuous improvements in patient care and workflow efficiency present many opportunities for AI implementation. Indeed, AI has already demonstrated capabilities for assisting in tasks such as documentation, image classification, and surgical outcome prediction. More specifically, this technology can be harnessed in facial plastic surgery, where the unique characteristics of the field lends itself well to specific applications. AI is not without its limitations, however, and the further adoption of AI in medicine and facial plastic surgery must necessarily be accompanied by discussion on the ethical implications and proper usage of AI in healthcare. In this article, we review current and potential uses of AI in facial plastic surgery, as well as its ethical ramifications.

Concordance in Breast Cancer Grading by Artificial Intelligence on Whole Slide Images Compares With a Multi-Institutional Cohort of Breast Pathologists.

CONTEXT.­: Breast carcinoma grade, as determined by the Nottingham Grading System (NGS), is an important criterion for determining prognosis. The NGS is based on 3 parameters: tubule formation (TF), nuclear pleomorphism (NP), and mitotic count (MC). The advent of digital pathology and artificial intelligence (AI) have increased interest in virtual microscopy using digital whole slide imaging (WSI) more broadly. OBJECTIVE.­: To compare concordance in breast carcinoma grading between AI and a multi-institutional group of breast pathologists using digital WSI. DESIGN.­: We have developed an automated NGS framework using deep learning. Six pathologists and AI independently reviewed a digitally scanned slide from 137 invasive carcinomas and assigned a grade based on scoring of the TF, NP, and MC. RESULTS.­: Interobserver agreement for the pathologists and AI for overall grade was moderate (κ = 0.471). Agreement was good (κ = 0.681), moderate (κ = 0.442), and fair (κ = 0.368) for grades 1, 3, and 2, respectively. Observer pair concordance for AI and individual pathologists ranged from fair to good (κ = 0.313-0.606). Perfect agreement was observed in 25 cases (27.4%). Interobserver agreement for the individual components was best for TF (κ = 0.471 each) followed by NP (κ = 0.342) and was worst for MC (κ = 0.233). There were no observed differences in concordance amongst pathologists alone versus pathologists + AI. CONCLUSIONS.­: Ours is the first study comparing concordance in breast carcinoma grading between a multi-institutional group of pathologists using virtual microscopy to a newly developed WSI AI methodology. Using explainable methods, AI demonstrated similar concordance to pathologists alone.

Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology Workload.

Standard of care diagnostic procedure for suspected skin cancer is microscopic examination of hematoxylin & eosin stained tissue by a pathologist. Areas of high inter-pathologist discordance and rising biopsy rates necessitate higher efficiency and diagnostic reproducibility. We present and validate a deep learning system which classifies digitized dermatopathology slides into 4 categories. The system is developed using 5,070 images from a single lab, and tested on an uncurated set of 13,537 images from 3 test labs, using whole slide scanners manufactured by 3 different vendors. The system's use of deep-learning-based confidence scoring as a criterion to consider the result as accurate yields an accuracy of up to 98%, and makes it adoptable in a real-world setting. Without confidence scoring, the system achieved an accuracy of 78%. We anticipate that our deep learning system will serve as a foundation enabling faster diagnosis of skin cancer, identification of cases for specialist review, and targeted diagnostic classifications.

Spatioangular Prefiltering for Multiview 3D Displays.

In this paper, we analyze the reproduction of light fields on multiview 3D displays. A three-way interaction between the input light field signal (which is often aliased), the joint spatioangular sampling grids of multiview 3D displays, and the interview light leakage in modern multiview 3D displays is characterized in the joint spatioangular frequency domain. Reconstruction of light fields by all physical 3D displays is prone to light leakage, which means that the reconstruction low-pass filter implemented by the display is too broad in the angular domain. As a result, 3D displays excessively attenuate angular frequencies. Our analysis shows that this reduces sharpness of the images shown in the 3D displays. In this paper, stereoscopic image recovery is recast as a problem of joint spatioangular signal reconstruction. The combination of the 3D display point spread function and human visual system provides the narrow-band low-pass filter which removes spectral replicas in the reconstructed light field on the multiview display. The nonideality of this filter is corrected with the proposed prefiltering. The proposed light field reconstruction method performs light field antialiasing as well as angular sharpening to compensate for the nonideal response of the 3D display. The union of cosets approach which has been used earlier by others is employed here to model the nonrectangular spatioangular sampling grids on a multiview display in a generic fashion. We confirm the effectiveness of our approach in simulation and in physical hardware, and demonstrate improvement over existing techniques.