An unclonable, fingerprint-mimicking anti-counterfeiting strategy is presented that encrypts polymeric particles with randomly generated silica film wrinkles. The generated wrinkle codes are as highly unique as human fingerprints and are technically irreproducible. Superior to previous physical unclonable functions, codes are tunable on demand and generable on various geometries. Reliable authentication of real-world products that have these microfingerprints is demonstrated using optical decoding methods.

A QR-coded microtaggant for the anti-counterfeiting of drugs is proposed that can provide high capacity and error-correction capability. It is fabricated lithographically in a microfluidic channel with special consideration of the island patterns in the QR Code. The microtaggant is incorporated in the drug capsule ("on-dose authentication") and can be read by a simple smartphone QR Code reader application when removed from the capsule and washed free of drug.

Patients with odontogenic cysts and tumors may have to undergo serious surgery unless the lesion is properly detected at the early stage. The purpose of this study is to evaluate the diagnostic performance of the real-time object detecting deep convolutional neural network You Only Look Once (YOLO) v2—a deep learning algorithm that can both detect and classify an object at the same time—on panoramic radiographs. In this study, 1602 lesions on panoramic radiographs taken from 2010 to 2019 at Yonsei University Dental Hospital were selected as a database. Images were classified and labeled into four categories: dentigerous cysts, odontogenic keratocyst, ameloblastoma, and no lesion. Comparative analysis among three groups (YOLO, oral and maxillofacial surgeons, and general practitioners) was done in terms of precision, recall, accuracy, and F1 score. While YOLO ranked highest among the three groups (precision = 0.707, recall = 0.680), the performance differences between the machine and clinicians were statistically insignificant. The results of this study indicate the usefulness of auto-detecting convolutional networks in certain pathology detection and thus morbidity prevention in the field of oral and maxillofacial surgery.

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