Artificial intelligence in dermatology: the ‘unsupervised’ learning

The State of Machine Learning in Outcomes Prediction of Transsphenoidal Surgery: A Systematic Review

The purpose of this analysis is to assess the use of machine learning (ML) algorithms in the prediction of postoperative outcomes, including complications, recurrence, and death in transsphenoidal surgery. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically reviewed all papers that used at least one ML algorithm to predict outcomes after transsphenoidal surgery. We searched Scopus, PubMed, and Web of Science databases for studies published prior to May 12, 2021. We identified 13 studies enrolling 5,048 patients. We extracted the general characteristics of each study; the sensitivity, specificity, area under the curve (AUC) of the ML models developed as well as the features identified as important by the ML models. We identified 12 studies with 5,048 patients that included ML algorithms for adenomas, three with 1807 patients specifically for acromegaly, and five with 2105 patients specifically for Cushing's disease. Nearly all were single-institution studies. The studies used a heterogeneous mix of ML algorithms and features to build predictive models. All papers reported an AUC greater than 0.7, which indicates clinical utility. ML algorithms have the potential to predict postoperative outcomes of transsphenoidal surgery and can improve patient care. Ensemble algorithms and neural networks were often top performers when compared with other ML algorithms. Biochemical and preoperative features were most likely to be selected as important by ML models. Inexplicability remains a challenge, but algorithms such as local interpretable model-agnostic explanation or Shapley value can increase explainability of ML algorithms. Our analysis shows that ML algorithms have the potential to greatly assist surgeons in clinical decision making.

Low power consumption photoelectric coupling perovskite memristor with adjustable threshold voltage

Organic-inorganic halide perovskites (OHPs) have been proven to possess unique optical and electrical properties, and achieved more extensive application as excellent materials for memristors in recent years. Based on the traditional OHP-based memristors, the intermediate layer of the memristor was prepared using yttrium oxide (Y₂O₃)/OHP stacking structure in this manuscript. The potential barrier between Y₂O₃and perovskite is relatively high (ΔE_C = 2.13 eV) which leads to comparatively low current of the memristor, thus the power consumption can be reduced. Besides, by changing the external light conditions, one can realize sharp or slow switch between high resistance state (HRS) and low resistance state (LRS), so as to meet the requirement of multilevel data storage, which indicates its promising application prospect in information storage and biological simulation. In addition, based on characteristics of photoelectric coupling, the Y₂O₃/OHP memristor can also achieve the advantage of adjustable threshold voltage. The transition of HRS and LRS can be realized by changing the illumination condition at any voltage, which means the set and reset voltage are not fixed, so that the memristor with adjustable threshold voltage can adapt to various working conditions.