The effect of charge-balanced transcutaneous electrical nerve stimulation on rodent facial nerve regeneration.

This study aimed to investigate the effect of charge-balanced transcutaneous electrical nerve stimulation (cb-TENS) in accelerating recovery of the facial function and nerve regeneration after facial nerve (FN) section in a rat model. The main trunk of the left FN was divided and immediately sutured just distal to the stylomastoid foramen in 66 Sprague-Dawley rats. The control group had no electrical stimulus. The other two groups received cb-TENS at 20 Hz (20 Hz group) or 40 Hz (40 Hz group). Cb-TENS was administered daily for seven days and then twice a week for three weeks thereafter. To assess the recovery of facial function, whisker movement was monitored for four weeks. Histopathological evaluation of nerve regeneration was performed using transmission electron microscopy (TEM) and confocal microscopy with immunofluorescence (IF) staining. In addition, the levels of various molecular biological markers that affect nerve regeneration were analyzed. Whisker movement in the cb-TENS groups showed faster and better recovery than the control group. The 40 Hz group showed significantly better movement at the first week after injury (p < 0.0125). In histopathological analyses using TEM, nerve axons and Schwann cells, which were destroyed immediately after the injury, recovered in all groups over time. However, the regeneration of the myelin sheath was remarkably rapid and thicker in the 20 Hz and 40 Hz groups than in the control group. Image analysis using IF staining showed that the expression levels of S100B and NF200 increased over time in all groups. Specifically, the expression of NF200 in the 20 Hz and 40 Hz groups increased markedly compared to the control group. The real-time polymerase chain reaction was performed on ten representative neurotrophic factors, and the levels of IL-1ß and IL-6 were significantly higher in the 20 and 40 Hz groups than in the control group (p < 0.015). Cb-TENS facilitated and accelerated FN recovery in the rat model, as it significantly reduced the recovery time for the whisker movement. The histopathological study and analysis of neurotrophic factors supported the role of cb-TENS in the enhanced regeneration of the FN.

Risk prediction platform for pancreatic fistula after pancreatoduodenectomy using artificial intelligence.

BACKGROUND: Despite advancements in operative technique and improvements in postoperative managements, postoperative pancreatic fistula (POPF) is a life-threatening complication following pancreatoduodenectomy (PD). There are some reports to predict POPF preoperatively or intraoperatively, but the accuracy of those is questionable. Artificial intelligence (AI) technology is being actively used in the medical field, but few studies have reported applying it to outcomes after PD. AIM: To develop a risk prediction platform for POPF using an AI model. METHODS: Medical records were reviewed from 1769 patients at Samsung Medical Center who underwent PD from 2007 to 2016. A total of 38 variables were inserted into AI-driven algorithms. The algorithms tested to make the risk prediction platform were random forest (RF) and a neural network (NN) with or without recursive feature elimination (RFE). The median imputation method was used for missing values. The area under the curve (AUC) was calculated to examine the discriminative power of algorithm for POPF prediction. RESULTS: The number of POPFs was 221 (12.5%) according to the International Study Group of Pancreatic Fistula definition 2016. After median imputation, AUCs using 38 variables were 0.68 ± 0.02 with RF and 0.71 ± 0.02 with NN. The maximal AUC using NN with RFE was 0.74. Sixteen risk factors for POPF were identified by AI algorithm: Pancreatic duct diameter, body mass index, preoperative serum albumin, lipase level, amount of intraoperative fluid infusion, age, platelet count, extrapancreatic location of tumor, combined venous resection, co-existing pancreatitis, neoadjuvant radiotherapy, American Society of Anesthesiologists' score, sex, soft texture of the pancreas, underlying heart disease, and preoperative endoscopic biliary decompression. We developed a web-based POPF prediction platform, and this application is freely available at http://popfrisk.smchbp.org. CONCLUSION: This study is the first to predict POPF with multiple risk factors using AI. This platform is reliable (AUC 0.74), so it could be used to select patients who need especially intense therapy and to preoperatively establish an effective treatment strategy.

Automated measurement of hydrops ratio from MRI in patients with Ménière's disease using CNN-based segmentation.

Ménière's Disease (MD) is difficult to diagnose and evaluate objectively over the course of treatment. Recently, several studies have reported MD diagnoses by MRI-based endolymphatic hydrops (EH) analysis. However, this method is time-consuming and complicated. Therefore, a fast, objective, and accurate evaluation tool is necessary. The purpose of this study was to develop an algorithm that can accurately analyze EH on intravenous (IV) gadolinium (Gd)-enhanced inner-ear MRI using artificial intelligence (AI) with deep learning. In this study, we developed a convolutional neural network (CNN)-based deep-learning model named INHEARIT (INner ear Hydrops Estimation via ARtificial InTelligence) for the automatic segmentation of the cochlea and vestibule, and calculation of the EH ratio in the segmented region. Measurement of the EH ratio was performed manually by a neuro-otologist and neuro-radiologist and by estimation with the INHEARIT model and were highly consistent (intraclass correlation coefficient = 0.971). This is the first study to demonstrate that automated EH ratio measurements are possible, which is important in the current clinical context where the usefulness of IV-Gd inner-ear MRI for MD diagnosis is increasing.

Development of an automatic muscle atrophy measuring algorithm to calculate the ratio of supraspinatus in supraspinous fossa using deep learning.

BACKGROUND AND OBJECTIVE: Rotator cuff muscle tear is one of the most frequent reason of operations in orthopedic surgery. There are several clinical indicators such as Goutallier grade and occupation ratio in the diagnosis and surgery of these diseases, but subjective intervention of the diagnosis is an obstacle in accurately detecting the correct region. METHODS: Therefore, in this paper, we propose a fully convolutional deep learning algorithm to quantitatively detect the fossa and muscle region by measuring the occupation ratio of supraspinatus in the supraspinous fossa. In the development and performance evaluation of the algorithm, 240 patients MRI dataset with various disease severities were included. RESULTS: As a result, the pixel-wise accuracy of the developed algorithm is 0.9984 ± 0.073 in the fossa region and 0.9988 ± 0.065 in the muscle region. The dice coefficient is 0.9718 ± 0.012 in the fossa region and 0.9463 ± 0.047 in the muscle region. CONCLUSIONS: We expect that the proposed convolutional neural network can improve the efficiency and objectiveness of diagnosis by quantifying the index used in the orthopedic rotator cuff tear.

A data-driven artificial intelligence model for remote triage in the prehospital environment.

In a mass casualty incident, the factors that determine the survival rate of injured patients are diverse, but one of the key factors is the time for triage. Additionally, the main factor that determines the time of triage is the number of medical personnel. However, when relying on a small number of medical personnel, the ability to increase survivability is limited. Therefore, developing a classification model for survival prediction that can quickly and precisely triage via wearable devices without medical personnel is important. In this study, we designed a consciousness index to substitute the factor by manpower and improved the classification accuracy by applying a machine learning algorithm. First, logistic regression analysis using vital signs and a consciousness index capable of remote monitoring through wearable devices confirmed the high efficiency of the consciousness index. We then developed a classification model with high accuracy which corresponds to existing injury severity scoring systems through the machine learning algorithms. We extracted 460,865 cases which met our criteria for developing the survival prediction from the national sample project in the national trauma databank which contains 408,316 cases of blunt injury and 52,549 cases of penetrating injury. Among the dataset, 17,918 (3.9%) cases died while the other survived. The AUCs with 95% confidence intervals (CIs) for the different models with the proposed simplified consciousness score as follows: RTS (as baseline), 0.78 (95% CI = 0.775 to 0.785); logistic regression, 0.87 (95% CI = 0.862 to 0.870); random forest, 0.87 (95% CI = 0.862 to 0.872); deep neural network, 0.89 (95% CI = 0.882 to 0.890). As a result, we confirmed the possibility of remote triage using a wearable device. It is expected that the time required for triage can be effectively reduced by using the developed classification model of survival prediction.

Detecting Bladder Biomarkers for Closed-Loop Neuromodulation: A Technological Review.

Neuromodulation was introduced for patients with poor outcomes from the existing traditional treatment approaches. It is well-established as an alternative, novel treatment option for voiding dysfunction. The current system of neuromodulation uses an open-loop system that only delivers continuous stimulation without considering the patient's state changes. Though the conventional open-loop system has shown positive clinical results, it can cause problems such as decreased efficacy over time due to neural habituation, higher risk of tissue damage, and lower battery life. Therefore, there is a need for a closed-loop system to overcome the disadvantages of existing systems. The closed-loop neuromodulation includes a system to monitor and stimulate micturition reflex pathways from the lower urinary tract, as well as the central nervous system. In this paper, we reviewed the current technological status to measure biomarker for closed-loop neuromodulation systems for voiding dysfunction.

Analysis of Nociceptive Information Encoded in the Temporal Discharge Patterns of Cutaneous C-Fibers.

The generation of pain signals from primary afferent neurons is explained by a labeled-line code. However, this notion cannot apply in a simple way to cutaneous C-fibers, which carry signals from a variety of receptors that respond to various stimuli including agonist chemicals. To represent the discharge patterns of C-fibers according to different agonist chemicals, we have developed a quantitative approach using three consecutive spikes. By using this method, the generation of pain in response to chemical stimuli is shown to be dependent on the temporal aspect of the spike trains. Furthermore, under pathological conditions, gamma-aminobutyric acid resulted in pain behavior without change of spike number but with an altered discharge pattern. Our results suggest that information about the agonist chemicals may be encoded in specific temporal patterns of signals in C-fibers, and nociceptive sensation may be influenced by the extent of temporal summation originating from the temporal patterns.

Touch stimulated pulse generation in biomimetic single-layer graphene.

Detecting variation in contact pressure is a separate sensing mode in the human somatosensory system that differs from the detection of pressure magnitude. If pressure magnitude and variation sensing can be achieved simultaneously, an advanced biomimetic tactile system that better emulates human senses may be developed. We report on a novel single-layer graphene based artificial mechanoreceptor that generates a resistance pulse as the contact stimulus passes a specific threshold pressure, mimicking the generation of action potentials in a biological fast-adapting mechanoreceptor. The electric field from a flexible membrane gate electrode placed above a graphene channel raises the Fermi level from the valence band as pressure deflects the membrane. The threshold pressure is reached when the Fermi level crosses the Dirac point in the graphene energy band, which generates a sharp peak in the measured resistance. We found that by changing the gate potential it was possible to modulate the threshold pressure and using a series of graphene channels, a train of pulses were generated during a transient pressurizing stimulus demonstrating biomimetic behaviour.

Enhanced beam-steering-based diagonal beamforming algorithm for binaural hearing support devices.

In order to improve speech intelligibility for hearing-impaired people in various listening situations, it is necessary to diversify the possible focusing directions of a beamformer. In a previous report, the concept of binaural beam-steering that can focus a beamformer in diagonal directions was applied to a binaural hearing aid; however, in the previously proposed protocol, the effective frequency range for consistent diagonal beam-steering was limited to the 200-750 Hz range, which is far narrower than that of normal speech signals (200-4000 Hz). In this study, we proposed a modified binaural diagonal beam-steering technique that can reduce the focusing-direction deviations at high input frequencies up to 4000 Hz by introducing a new correction factor to the original protocol that can reduce the differences in gradient between the signal and the noise components at frequencies up to 4000 Hz. In simulation tests, the focusing effect of the proposed algorithm was more consistent than conventional algorithms. The deviations between the target and the focusing directions were reduced 27% in the left device and 6% in the right device with 45° steering at a 4000 Hz input signal, and were reduced 3% in the left device and 25% in the right device with 135° steering. On the basis of the experimental results, we believe that the proposed algorithm has the potential to help hearing-impaired people in various listening situations.