Machine learning and big data provide crucial insight for future biomaterials discovery and research.

Machine learning have been widely adopted in a variety of fields including engineering, science, and medicine revolutionizing how data is collected, used, and stored. Their implementation has led to a drastic increase in the number of computational models for the prediction of various numerical, categorical, or association events given input variables. We aim to examine recent advances in the use of machine learning when applied to the biomaterial field. Specifically, quantitative structure properties relationships offer the unique ability to correlate microscale molecular descriptors to larger macroscale material properties. These new models can be broken down further into four categories: regression, classification, association, and clustering. We examine recent approaches and new uses of machine learning in the three major categories of biomaterials: metals, polymers, and ceramics for rapid property prediction and trend identification. While current research is promising, limitations in the form of lack of standardized reporting and available databases complicates the implementation of described models. Herein, we hope to provide a snapshot of the current state of the field and a beginner's guide to navigating the intersection of biomaterials research and machine learning. STATEMENT OF SIGNIFICANCE: Machine learning and its methods have found a variety of uses beyond the field of computer science but have largely been neglected by those in realm of biomaterials. Through the use of more computational methods, biomaterials development can be expediated while reducing the need for standard trial and error methods. Within, we introduce four basic models that readers can potentially apply to their current research as well as current applications within the field. Furthermore, we hope that this article may act as a "call to action" for readers to realize and address the current lack of implementation within the biomaterials field.

Treatment of Cutaneous Squamous Cell Carcinoma In Situ With Curettage Followed by Topical Imiquimod 5% Cream.

BACKGROUND: Treatment strategies for cutaneous squamous cell carcinoma in situ (cSCCIS) are many but reported cure rates are variable and few studies report 5-year follow-up data. OBJECTIVE: To evaluate the treatment of cSCCIS by curettage followed by topical imiquimod 5% cream (C&I). METHODS: We evaluated all immunocompetent patients with biopsy proven cSCCIS treated by C&I between January 2008 and December 2012. RESULTS: A total of 861 patients with 1,198 cSCCIS were treated, with median follow-up of 71 months. The mean tumor diameter was 10.2 mm. The average duration of treatment with imiquimod 5% cream was 21 days. Kaplan-Meier estimated recurrence-free survival at 5-year follow-up was 99.71% with 95% CI (99.38%, 100.00%). A follow-up questionnaire returned by 45% of patients revealed that 94% were satisfied with their treatment. Six hundred eleven patients developed a new nonmelanoma skin cancer (NMSC) during the follow-up period, and 91% (556/611) of patients chose this combination treatment for at least one new NMSC. CONCLUSION: The combination treatment for cSCCIS of C&I had less than 1% cumulative probability of treatment failure at 5 years. Patients tolerated the treatment well, with the majority choosing this method of treatment for at least one new NMSC.