Improving taxonomic classification with feature space balancing.

Summary: Modern high-throughput sequencing technologies, such as metagenomic sequencing, generate millions of sequences that need to be assigned to their taxonomic rank. Modern approaches either apply local alignment to existing databases, such as MMseqs2, or use deep neural networks, as in DeepMicrobes and BERTax. Due to the increasing size of datasets and databases, alignment-based approaches are expensive in terms of runtime. Deep learning-based approaches can require specialized hardware and consume large amounts of energy. In this article, we propose to use k-mer profiles of DNA sequences as features for taxonomic classification. Although k-mer profiles have been used before, we were able to significantly increase their predictive power significantly by applying a feature space balancing approach to the training data. This greatly improved the generalization quality of the classifiers. We have implemented different pipelines using our proposed feature extraction and dataset balancing in combination with different simple classifiers, such as bagged decision trees or feature subspace KNNs. By comparing the performance of our pipelines with state-of-the-art algorithms, such as BERTax and MMseqs2 on two different datasets, we show that our pipelines outperform these in almost all classification tasks. In particular, sequences from organisms that were not part of the training were classified with high precision. Availability and implementation: The open-source code and the code to reproduce the results is available in Seafile, at https://tinyurl.com/ysk47fmr. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Chatbots for future docs: exploring medical students' attitudes and knowledge towards artificial intelligence and medical chatbots.

Artificial intelligence (AI) in medicine and digital assistance systems such as chatbots will play an increasingly important role in future doctor - patient communication. To benefit from the potential of this technical innovation and ensure optimal patient care, future physicians should be equipped with the appropriate skills. Accordingly, a suitable place for the management and adaptation of digital assistance systems must be found in the medical education curriculum. To determine the existing levels of knowledge of medical students about AI chatbots in particular in the healthcare setting, this study surveyed medical students of the University of Luebeck and the University Hospital of Tuebingen. Using standardized quantitative questionnaires and qualitative analysis of group discussions, the attitudes of medical students toward AI and chatbots in medicine were investigated. From this, relevant requirements for the future integration of AI into the medical curriculum could be identified. The aim was to establish a basic understanding of the opportunities, limitations, and risks, as well as potential areas of application of the technology. The participants (N = 12) were able to develop an understanding of how AI and chatbots will affect their future daily work. Although basic attitudes toward the use of AI were positive, the students also expressed concerns. There were high levels of agreement regarding the use of AI in administrative settings (83.3%) and research with health-related data (91.7%). However, participants expressed concerns that data protection may be insufficiently guaranteed (33.3%) and that they might be increasingly monitored at work in the future (58.3%). The evaluations indicated that future physicians want to engage more intensively with AI in medicine. In view of future developments, AI and data competencies should be taught in a structured way during the medical curriculum and integrated into curricular teaching.

Differential privacy for eye tracking with temporal correlations.

New generation head-mounted displays, such as VR and AR glasses, are coming into the market with already integrated eye tracking and are expected to enable novel ways of human-computer interaction in numerous applications. However, since eye movement properties contain biometric information, privacy concerns have to be handled properly. Privacy-preservation techniques such as differential privacy mechanisms have recently been applied to eye movement data obtained from such displays. Standard differential privacy mechanisms; however, are vulnerable due to temporal correlations between the eye movement observations. In this work, we propose a novel transform-coding based differential privacy mechanism to further adapt it to the statistics of eye movement feature data and compare various low-complexity methods. We extend the Fourier perturbation algorithm, which is a differential privacy mechanism, and correct a scaling mistake in its proof. Furthermore, we illustrate significant reductions in sample correlations in addition to query sensitivities, which provide the best utility-privacy trade-off in the eye tracking literature. Our results provide significantly high privacy without any essential loss in classification accuracies while hiding personal identifiers.

RemoteEye: An open-source high-speed remote eye tracker : Implementation insights of a pupil- and glint-detection algorithm for high-speed remote eye tracking.

The increasing employment of eye-tracking technology in different application areas and in vision research has led to an increased need to measure fast eye-movement events. Whereas the cost of commercial high-speed eye trackers (above 300 Hz) is usually in the tens of thousands of EUR, to date, only a small number of studies have proposed low-cost solutions. Existing low-cost solutions however, focus solely on lower frame rates (up to 120 Hz) that might suffice for basic eye tracking, leaving a gap when it comes to the investigation of high-speed saccadic eye movements. In this paper, we present and evaluate a system designed to track such high-speed eye movements, achieving operating frequencies well beyond 500 Hz. This includes methods to effectively and robustly detect and track glints and pupils in the context of high-speed remote eye tracking, which, paired with a geometric eye model, achieved an average gaze estimation error below 1 degree and average precision of 0.38 degrees. Moreover, average undetection rate was only 0.33%. At a total investment of less than 600 EUR, the proposed system represents a competitive and suitable alternative to commercial systems at a tiny fraction of the cost, with the additional advantage that it can be freely tuned by investigators to fit their requirements independent of eye-tracker vendors.

Optimal eye movement strategies: a comparison of neurosurgeons gaze patterns when using a surgical microscope.

BACKGROUND: Previous studies have consistently demonstrated gaze behaviour differences related to expertise during various surgical procedures. In micro-neurosurgery, however, there is a lack of evidence of empirically demonstrated individual differences associated with visual attention. It is unknown exactly how neurosurgeons see a stereoscopic magnified view in the context of micro-neurosurgery and what this implies for medical training. METHOD: We report on an investigation of the eye movement patterns in micro-neurosurgery using a state-of-the-art eye tracker. We studied the eye movements of nine neurosurgeons while performing cutting and suturing tasks under a surgical microscope. Eye-movement characteristics, such as fixation (focus level) and saccade (visual search pattern), were analysed. RESULTS: The results show a strong relationship between the level of microsurgical skill and the gaze pattern, whereas more expertise is associated with greater eye control, stability, and focusing in eye behaviour. For example, in the cutting task, well-trained surgeons increased their fixation durations on the operating field twice as much as the novices (expert, 848 ms; novice, 402 ms). CONCLUSIONS: Maintaining steady visual attention on the target (fixation), as well as being able to quickly make eye jumps from one target to another (saccades) are two important elements for the success of neurosurgery. The captured gaze patterns can be used to improve medical education, as part of an assessment system or in a gaze-training application.

Ways of improving the precision of eye tracking data: Controlling the influence of dirt and dust on pupil detection.

Eye-tracking technology has to date been primarily employed in research. With recent advances in affordable video-based devices, the implementation of gaze-aware smartphones, and marketable driver monitoring systems, a considerable step towards pervasive eye-tracking has been made. However, several new challenges arise with the usage of eye-tracking in the wild and will need to be tackled to increase the acceptance of this technology. The main challenge is still related to the usage of eye-tracking together with eyeglasses, which in combination with reflections for changing illumination conditions will make a subject "untrackable". If we really want to bring the technology to the consumer, we cannot simply exclude 30% of the population as potential users only because they wear eyeglasses, nor can we make them clean their glasses and the device regularly. Instead, the pupil detection algorithms need to be made robust to potential sources of noise. We hypothesize that the amount of dust and dirt on the eyeglasses and the eye-tracker camera has a significant influence on the performance of currently available pupil detection algorithms. Therefore, in this work, we present a systematic study of the effect of dust and dirt on the pupil detection by simulating various quantities of dirt and dust on eyeglasses. Our results show 1) an overall high robustness to dust in an offfocus layer. 2) the vulnerability of edge-based methods to even small in-focus dust particles. 3) a trade-off between tolerated particle size and particle amount, where a small number of rather large particles showed only a minor performance impact.

Non-intrusive practitioner pupil detection for unmodified microscope oculars.

Modern microsurgery is a long and complex task requiring the surgeon to handle multiple microscope controls while performing the surgery. Eye tracking provides an additional means of interaction for the surgeon that could be used to alleviate this situation, diminishing surgeon fatigue and surgery time, thus decreasing risks of infection and human error. In this paper, we introduce a novel algorithm for pupil detection tailored for eye images acquired through an unmodified microscope ocular. The proposed approach, the Hough transform, and six state-of-the-art pupil detection algorithms were evaluated on over 4000 hand-labeled images acquired from a digital operating microscope with a non-intrusive monitoring system for the surgeon eyes integrated. Our results show that the proposed method reaches detection rates up to 71% for an error of ≈3% w.r.t the input image diagonal; none of the state-of-the-art pupil detection algorithms performed satisfactorily. The algorithm and hand-labeled data set can be downloaded at:: www.ti.uni-tuebingen.de/perception.