Noninvasive three-state sleep-wake staging in mice using electric field sensors.

STUDY OBJECTIVE: Validate a novel method for sleep-wake staging in mice using noninvasive electric field (EF) sensors. METHODS: Mice were implanted with electroencephalogram (EEG) and electromyogram (EMG) electrodes and housed individually. Noninvasive EF sensors were attached to the exterior of each chamber to record respiration and other movement simultaneously with EEG, EMG, and video. A sleep-wake scoring method based on EF sensor data was developed with reference to EEG/EMG and then validated by three expert scorers. Additionally, novice scorers without sleep-wake scoring experience were self-trained to score sleep using only the EF sensor data, and results were compared to those from expert scorers. Lastly, ability to capture three-state sleep-wake staging with EF sensors attached to traditional mouse home-cages was tested. RESULTS: EF sensors quantified wake, rapid eye movement (REM) sleep, and non-REM sleep with high agreement (>93%) and comparable inter- and intra-scorer error as EEG/EMG. Novice scorers successfully learned sleep-wake scoring using only EF sensor data and scoring criteria, and achieved high agreement with expert scorers (>91%). When applied to traditional home-cages, EF sensors enabled classification of three-state (wake, NREM and REM) sleep-wake independent of EEG/EMG. CONCLUSIONS: EF sensors score three-state sleep-wake architecture with high agreement to conventional EEG/EMG sleep-wake scoring 1) without invasive surgery, 2) from outside the home-cage, and 3) and without requiring specialized training or equipment. EF sensors provide an alternative method to assess rodent sleep for animal models and research laboratories in which EEG/EMG is not possible or where noninvasive approaches are preferred.

Dermoscopy diagnosis of cancerous lesions utilizing dual deep learning algorithms via visual and audio (sonification) outputs: Laboratory and prospective observational studies.

BACKGROUND: Early diagnosis of skin cancer lesions by dermoscopy, the gold standard in dermatological imaging, calls for a diagnostic upscale. The aim of the study was to improve the accuracy of dermoscopic skin cancer diagnosis through use of novel deep learning (DL) algorithms. An additional sonification-derived diagnostic layer was added to the visual classification to increase sensitivity. METHODS: Two parallel studies were conducted: a laboratory retrospective study (LABS, n = 482 biopsies) and a non-interventional prospective observational study (OBS, n = 63 biopsies). A training data set of biopsy-verified reports, normal and cancerous skin lesions (n = 3954), were used to develop a DL classifier exploring visual features (System A). The outputs of the classifier were sonified, i.e. data conversion into sound (System B). Derived sound files were analyzed by a second machine learning classifier, either as raw audio (LABS, OBS) or following conversion into spectrograms (LABS) and by image analysis and human heuristics (OBS). The OBS criteria outcomes were System A specificity and System B sensitivity as raw sounds, spectrogram areas or heuristics. FINDINGS: LABS employed dermoscopies, half benign half malignant, and compared the accuracy of Systems A and B. System A algorithm resulted in a ROC AUC of 0.976 (95% CI, 0.965-0.987). Secondary machine learning analysis of raw sound, FFT and Spectrogram ROC curves resulted in AUC's of 0.931 (95% CI 0.881-0.981), 0.90 (95% CI 0.838-0.963) and 0.988 (CI 95% 0.973-1.001), respectively. OBS analysis of raw sound dermoscopies by the secondary machine learning resulted in a ROC AUC of 0.819 (95% CI, 0.7956 to 0.8406). OBS image analysis of AUC for spectrograms displayed a ROC AUC of 0.808 (CI 95% 0.6945 To 0.9208). By applying a heuristic analysis of Systems A and B a sensitivity of 86% and specificity of 91% were derived in the clinical study. INTERPRETATION: Adding a second stage of processing, which includes a deep learning algorithm of sonification and heuristic inspection with machine learning, significantly improves diagnostic accuracy. A combined two-stage system is expected to assist clinical decisions and de-escalate the current trend of over-diagnosis of skin cancer lesions as pathological. FUND: Bostel Technologies. Trial Registration clinicaltrials.gov Identifier: NCT03362138.

Monitoring dressing activity failures through RFID and video.

BACKGROUND: Monitoring and evaluation of Activities of Daily Living in general, and dressing activity in particular, is an important indicator in the evaluation of the overall cognitive state of patients. In addition, the effectiveness of therapy in patients with motor impairments caused by a stroke, for example, can be measured through long-term monitoring of dressing activity. However, automatic monitoring of dressing activity has not received significant attention in the current literature. OBJECTIVES: Considering the importance of monitoring dressing activity, the main goal of this work was to investigate the possibility of recognizing dressing activities and automatically identifying common failures exhibited by patients suffering from motor or cognitive impairments. METHODS: The system developed for this purpose comprised analysis of RFID (radio frequency identification) tracking and computer vision processing. Eleven test subjects, not connected to the research, were recruited and asked to perform the dressing task by choosing any combination of clothes without further assistance. Initially the test subjects performed correct dressing and then they were free to choose from a set of dressing failures identified from the current research literature. RESULTS: The developed system was capable of automatically recognizing common dressing failures. In total, there were four dressing failures observed for upper garments and three failures for lower garments, in addition to recognizing successful dressing. The recognition rate for identified dressing failures was between 80% and 100%. CONCLUSIONS: We developed a robust system to monitor the dressing activity. Given the importance of monitoring the dressing activity as an indicator of both cognitive and motor skills the system allows for the possibility of long term tracking and continuous evaluation of the dressing task. Long term monitoring can be used in rehabilitation and cognitive skills evaluation.