We present the development of an automatic dental diagnosis system based on artificial intelligence. The goal is to accurately support clinical decision-making in dentistry based on pantomographic images. We describe the construction of a large dataset of pantomographic images with high-quality annotations, the development of an intelligent model for semantic image interpretation and generation of presumptive diagnoses, and an application for end users. The model demonstrates the ability to correctly identify each individual tooth, detect multiple conditions and common pathologies in dental pieces, in case they arise, and generate a presumptive diagnostic text based on the information extracted from the image. The application, in the form of a digital web-based negatoscope, represents a technological advancement in radiology in line with the digital transformation era, offering a versatile and efficient tool for the interactive visualization of radiographic images, especially designed for dental professionals.

This report describes The University of Texas Student Cluster Competition team’s effort to reproduce the results of “MemXCT: memory-centric X-ray CT reconstruction with massive parallelization” (Hidayetoğluet al., 2019). The article details a new memory-centric approach that reconstructs X-ray computed tomography (XCT) from noisy raw data. In our reproduction experiments, we utilized Microsoft Azure’s CycleCloud tool to provision, orchestrate, and manage our computing cluster in the cloud. In particular, we scheduled and benchmarked reconstruction workloads using Azure’s CPU-based HC44rs and GPU-based NC12s v2 virtual machine (VM) types to evaluate the scalability properties of the reconstruction approach and the performance differences between architectures. The HC44rs VMs contained 44 Intel Xeon Platinum cores, while the NC12s v2 VM was equipped with two NVIDIA P100 GPUs. We used a recent version of Intel’s compiler stack with the MKL library for our CPU code along with CUDA 11.1 on GPUs. Overall, our results confirm the findings of the original article, demonstrating similar acceleration on GPUs and scalability properties on CPUs. Digital artifacts from these experiments are available at: 10.5281/zenodo.5598108

Identifying the accurate location of small objects is a key element of Internet of Things (IoT). This paper investigates the feasibility of tracking the real-time location of a completely passive retroreflector using visible light for IoT applications. Existing ultra-low power visible light retroreflector systems modulate light with a liquid crystal display (LCD) shutter, which is powered by a solar cell. However, the solar cell costs additional space exclusively for energy harvesting, and it may not be able to supply enough power to the advanced power-hungry LCD shutter and its driver circuit. To eliminate the power supply, we design, implement and evaluate PassiveRETRO, an enhanced retroreflector-based visible light localization system. The PassiveRETRO system completely eliminates the necessity of any electronic component on the IoT devices. Polarization-based modulation and bandpass optical filters are adopted to identify the retroreflected optical signal and create multiple channels. Each IoT device operates on a specific range of the visible light spectrum. Optical rotatory dispersion is further applied to mitigate the mutual interference among different channels. Experimental results from our prototyped system show that PassiveRETRO is robust to environmental reflection and can still achieve centimeter-level location accuracy when multiple IoT devices are deployed.