Performance comparison between two computer-aided detection colonoscopy models by trainees using different false positive thresholds: a cross-sectional study in Thailand.

BACKGROUND/AIMS: This study aims to compare polyp detection performance of "Deep-GI," a newly developed artificial intelligence (AI) model, to a previously validated AI model computer-aided polyp detection (CADe) using various false positive (FP) thresholds and determining the best threshold for each model. METHODS: Colonoscopy videos were collected prospectively and reviewed by three expert endoscopists (gold standard), trainees, CADe (CAD EYE; Fujifilm Corp.), and Deep-GI. Polyp detection sensitivity (PDS), polyp miss rates (PMR), and false-positive alarm rates (FPR) were compared among the three groups using different FP thresholds for the duration of bounding boxes appearing on the screen. RESULTS: In total, 170 colonoscopy videos were used in this study. Deep-GI showed the highest PDS (99.4% vs. 85.4% vs. 66.7%, p<0.01) and the lowest PMR (0.6% vs. 14.6% vs. 33.3%, p<0.01) when compared to CADe and trainees, respectively. Compared to CADe, Deep-GI demonstrated lower FPR at FP thresholds of ≥0.5 (12.1 vs. 22.4) and ≥1 second (4.4 vs. 6.8) (both p<0.05). However, when the threshold was raised to ≥1.5 seconds, the FPR became comparable (2 vs. 2.4, p=0.3), while the PMR increased from 2% to 10%. CONCLUSION: Compared to CADe, Deep-GI demonstrated a higher PDS with significantly lower FPR at ≥0.5- and ≥1-second thresholds. At the ≥1.5-second threshold, both systems showed comparable FPR with increased PMR.

Improvement of adenoma detection rate by two computer-aided colonic polyp detection systems in high adenoma detectors: a randomized multicenter trial.

BACKGROUND: This study aimed to evaluate the benefits of a self-developed computer-aided polyp detection system (SD-CADe) and a commercial system (CM-CADe) for high adenoma detectors compared with white-light endoscopy (WLE) as a control. METHODS: Average-risk 50-75-year-old individuals who underwent screening colonoscopy at five referral centers were randomized to SD-CADe, CM-CADe, or WLE groups (1:1:1 ratio). Trainees and staff with an adenoma detection rate (ADR) of ≥35% were recruited. The primary outcome was ADR. Secondary outcomes were the proximal adenoma detection rate (pADR), advanced adenoma detection rate (AADR), and the number of adenomas, proximal adenomas, and advanced adenomas per colonoscopy (APC, pAPC, and AAPC, respectively). RESULTS: The study enrolled 1200 participants. The ADR in the control, CM-CADe, and SD-CADe groups was 38.3%, 50.0%, and 54.8%, respectively. The pADR was 23.0%, 32.3%, and 38.8%, respectively. AADR was 6.0%, 10.3%, and 9.5%, respectively. After adjustment, the ADR and pADR in both intervention groups were significantly higher than in controls (all P<0.05). The APC in the control, CM-CADe, and SD-CADe groups was 0.66, 1.04, and 1.16, respectively. The pAPC was 0.33, 0.53, and 0.64, respectively, and the AAPC was 0.07, 0.12, and 0.10, respectively. Both CADe systems showed significantly higher APC and pAPC than WLE. AADR and AAPC were improved in both CADe groups versus control, although the differences were not statistically significant. CONCLUSION: Even in high adenoma detectors, CADe significantly improved ADR and APC. The AADR tended to be higher with both systems, and this may enhance colorectal cancer prevention.

Explainable Depression Detection Based on Facial Expression Using LSTM on Attentional Intermediate Feature Fusion with Label Smoothing.

Machine learning is used for a fast pre-diagnosis approach to prevent the effects of Major Depressive Disorder (MDD). The objective of this research is to detect depression using a set of important facial features extracted from interview video, e.g., radians, gaze at angles, action unit intensity, etc. The model is based on LSTM with an attention mechanism. It aims to combine those features using the intermediate fusion approach. The label smoothing was presented to further improve the model's performance. Unlike other black-box models, the integrated gradient was presented as the model explanation to show important features of each patient. The experiment was conducted on 474 video samples collected at Chulalongkorn University. The data set was divided into 134 depressed and 340 non-depressed categories. The results showed that our model is the winner, with a 88.89% F1-score, 87.03% recall, 91.67% accuracy, and 91.40% precision. Moreover, the model can capture important features of depression, including head turning, no specific gaze, slow eye movement, no smiles, frowning, grumbling, and scowling, which express a lack of concentration, social disinterest, and negative feelings that are consistent with the assumptions in the depressive theories.

Real-time gastric intestinal metaplasia diagnosis tailored for bias and noisy-labeled data with multiple endoscopic imaging.

This work presents real-time segmentation viz. gastric intestinal metaplasia (GIM). Recently, GIM segmentation of endoscopic images has been carried out to differentiate GIM from a healthy stomach. However, real-time detection is difficult to achieve. Conditions are challenging, and include multiple color modes (white light endoscopy and narrow-band imaging), other abnormal lesions (erosion and ulcer), noisy labels etc. Herein, our model is based on BiSeNet and can overcome the many issues regarding GIM. Application of auxiliary head and additional loss are seen to improve performance as well as enhance multiple color modes accurately. Further, multiple pre-processing techniques are utilized for leveraging detection performance: namely, location-wise negative sampling, jigsaw augmentation, and label smoothing. Finally, the decision threshold can be adjusted separately for each color mode. Work undertaken at King Chulalongkorn Memorial Hospital examined 940 histologically proven GIM images and 1239 non-GIM images, obtained over 173 frames per second (FPS). In terms of accuracy, our model is seen to outperform all baselines. Our results demonstrate sensitivity, specificity, positive predictive, negative predictive, accuracy, and mean intersection over union (IoU), achieving GIM segmentation values of 91%, 96%, 91%, 91%, 96%, and 55%, respectively.

Residual effects of combined vibratory and plantar stimulation while seated influences plantar pressure and spatiotemporal gait measures in individuals with Parkinson's disease exhibiting freezing of gait.

Introduction: Combined plantar pressure and vibratory stimulation has been shown to decrease freezing of gait (FOG) episodes and improve spatiotemporal gait parameters compared to single stimulation in Parkinson's disease (PD) patients with FOG. However, the effect of combined plantar stimulations on plantar pressure analysis has never been explored. Methods: Forty PD patients with frequent FOG were allocated to either FOG shoes embedded with a 100 Hz vibratory stimulation at the Achilles tendons and a soft thickened silicone pad at the hallux and sole, or sham shoes with a non-working vibratory motor and a flat non-pressure silicone pad (20 patients per arm) while seated for 96 s. The objective gait and plantar pressure analysis were measured immediately after the stimulation. Outcomes included the normalized percentage of changes in percent FOG (%FOG) and plantar pressure in the heel-strike and push-off phase that were compared between pre- and post-stimulations. Results: The FOG shoes group showed significantly decreased %FOG (81.5 ± 28.9% vs. 6.8 ± 22.1%, p < 0.001), plantar pressure in the heel-strike (47.8 ± 43.7% vs. 4.3 ± 9.8%, p < 0.001), plantar pressure in the push-off (57.7 ± 59.6% vs. 6.2 ± 11.6%, p < 0.001), force time integral (FTI) (40.9 ± 32.5% vs. 6.6 ± 17.3%, p < 0.001), and decreased heel contact time (19.3 ± 12.3% vs. 22.7 ± 32.5%, p < 0.001) when compared to the sham group. There was a strong negative correlation between %FOG and peak plantar pressure (r = -0.440, p = 0.005), plantar pressure in the heel-strike (r = -0.847, p < 0.001). Conclusion: Our study demonstrated that the FOG shoe could decrease FOG episodes by improving the heel-strike pressure, toe push-off and normalized heel-to-toe plantar pressure, suggesting that modification inputs from the peripheral sensory systems might significant improvement in FOG in PD.

Vibratory and plantar pressure stimulation: Steps to improve freezing of gait in Parkinson's disease.

INTRODUCTION: Freezing of gait (FOG) is a devastating symptom that develops in patients with advanced Parkinson's disease (PD) and is often unresponsive to pharmacological treatment. Recent research suggests that FOG may result from dysfunctional plantar peripheral sensory systems. The impact of combined plantar pressure and vibratory stimulation over vibratory or pressure alone on FOG remains unexplored. METHODS: PD patients with FOG were randomised into four groups and treated with combined vibratory and pressure stimulation, vibratory stimulation alone, pressure stimulation alone, or controls (no stimulation). Vibratory stimulation targeted both Achilles' tendons. Simultaneous bilateral pressure stimulation was applied to the first hallux, first metatarsal bone, and the sole. The primary outcome included normalized percent changes in percent FOG measured both pre- and immediately post-stimulation. Other outcomes including clinical rating scale, response to questionnaires, number and duration of freezing episodes, and spatiotemporal gait parameters at pre- and freezing episodes were also explored. RESULTS: Sixty PD patients participated in the study. Patients who were treated with combined vibratory and pressure stimulation responded with significant decreases in normalized percent changes of percent FOG (62.75 ± 25.54%, p < 0.001) compared with those treated with vibration alone (11.38 ± 8.29%, p < 0.001), pressure alone (15.15 ± 16.18%, p < 0.001), or controls (8.59 ± 16.85%, p < 0.001). CONCLUSION: Our study demonstrated the benefit of combined vibratory and pressure stimulation on FOG suggesting that this strategy might be developed as a novel treatment modality for PD patients with FOG.

Real-time semantic segmentation of gastric intestinal metaplasia using a deep learning approach.

BACKGROUND/AIMS: Previous artificial intelligence (AI) models attempting to segment gastric intestinal metaplasia (GIM) areas have failed to be deployed in real-time endoscopy due to their slow inference speeds. Here, we propose a new GIM segmentation AI model with inference speeds faster than 25 frames per second that maintains a high level of accuracy. METHODS: Investigators from Chulalongkorn University obtained 802 histological-proven GIM images for AI model training. Four strategies were proposed to improve the model accuracy. First, transfer learning was employed to the public colon datasets. Second, an image preprocessing technique contrast-limited adaptive histogram equalization was employed to produce clearer GIM areas. Third, data augmentation was applied for a more robust model. Lastly, the bilateral segmentation network model was applied to segment GIM areas in real time. The results were analyzed using different validity values. RESULTS: From the internal test, our AI model achieved an inference speed of 31.53 frames per second. GIM detection showed sensitivity, specificity, positive predictive, negative predictive, accuracy, and mean intersection over union in GIM segmentation values of 93%, 80%, 82%, 92%, 87%, and 57%, respectively. CONCLUSION: The bilateral segmentation network combined with transfer learning, contrast-limited adaptive histogram equalization, and data augmentation can provide high sensitivity and good accuracy for GIM detection and segmentation.

Parkinson's disease severity clustering based on tapping activity on mobile device.

In this study, we investigated the relationship between finger tapping tasks on the smartphone and the MDS-UPDRS I-II and PDQ-8 using the mPower dataset. mPower is a mobile application-based study for monitoring key indicators of PD progression and diagnosis. Currently, it is one of the largest, open access, mobile Parkinson's Disease studies. Data from seven modules with a total of 8,320 participants who provided the data of at least one task were released to the public researcher. The modules comprise demographics, MDS-UPDRS I-II, PDQ-8, memory, tapping, voice, and walking. Finger-tapping is one of the tasks that easy to perform and has been analyzed for the quantitative measurement of PD. Therefore, participants who performed both the tapping activity and MDS-UPDRS I-II rating scale were selected for our analysis. Note that the MDS-UPDRS mPower Survey only contains parts of the original scale and has not been clinimetrically tested for validity and reliability. We obtained a total of 1851 samples that contained the tapping activity and MDS-UPDRS I-II for the analysis. Nine features were selected to represent tapping activity. K-mean was applied as an unsupervised clustering algorithm in our study. For determining the number of clusters, the elbow method, Sihouette score, and Davies-Bouldin index, were employed as supporting evaluation metrics. Based on these metrics and expert opinion, we decide that three clusters were appropriate for our study. The statistical analysis found that the tapping features could separate participants into three severity groups. Each group has different characteristics and could represent different PD severity based on the MDS-UPDRS I-II and PDQ-8 scores. Currently, the severity assessment of a movement disorder is based on clinical observation. Therefore, it is highly dependant on the skills and experiences of the trained movement disorder specialist who performs the procedure. We believe that any additional methods that could potentially assist with quantitative assessment of disease severity, without the need for a clinical visit would be beneficial to both the healthcare professionals and patients.

Using Machine Learning for Predicting the Best Outcomes With Electrical Muscle Stimulation for Tremors in Parkinson's Disease.

Recent studies have identified that peripheral stimulation in Parkinson's disease (PD) is effective in tremor reduction, indicating that a peripheral feedback loop plays an important role in the tremor reset mechanism. This was an open-label, quasi-experimental, pre- and post-test design, single-blind, single-group study involving 20 tremor-dominant PD patients. The objective of this study is to explore the effect of electrical muscle stimulation (EMS) as an adjunctive treatment for resting tremor during "on" period and to identify the best machine learning model to predict the suitable stimulation level that will yield the longest period of tremor reduction or tremor reset time. In this study, we used a Parkinson's glove to evaluate, stimulate, and quantify the tremors of PD patients. This adjustable glove incorporates a 3-axis gyroscope to measure tremor signals and an EMS to provide an on-demand muscle stimulation to suppress tremors. Machine learning models were applied to identify the suitable pulse amplitude (stimulation level) in five classes that led to the longest tremor reset time. The study was registered at the www.clinicaltrials.gov under the name "The Study of Rest Tremor Suppression by Using Electrical Muscle Stimulation" (NCT02370108). Twenty tremor-dominant PD patients were recruited. After applying an average pulse amplitude of 6.25 (SD 2.84) mA and stimulation period of 440.7 (SD 560.82) seconds, the total time of tremor reduction, or tremor reset time, was 329.90 (SD 340.91) seconds. A significant reduction in tremor parameters during stimulation was demonstrated by a reduction of Unified Parkinson's Disease Rating Scale (UPDRS) scores, and objectively, with a reduction of gyroscopic data (p < 0.05, each). None of the subjects reported any serious adverse events. We also compared gyroscopic data with five machine learning techniques: Logistic Regression, Random Forest, Support Vector Machine (SVM), Neural Network (NN), and Long-Short-Term-Memory (LSTM). The machine learning model that gave the highest accuracy was LSTM, which obtained: accuracy = 0.865 and macro-F1 = 0.736. This study confirms the efficacy of EMS in the reduction of resting tremors in PD. LSTM was identified as the most effective model for predicting pulse amplitude that would elicit the longest tremor reset time. Our study provides further insight on the tremor reset mechanism in PD.

Relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual representations.

BACKGROUND: The Bacteria Biotope (BB) task is a biomedical relation extraction (RE) that aims to study the interaction between bacteria and their locations. This task is considered to pertain to fundamental knowledge in applied microbiology. Some previous investigations conducted the study by applying feature-based models; others have presented deep-learning-based models such as convolutional and recurrent neural networks used with the shortest dependency paths (SDPs). Although SDPs contain valuable and concise information, some parts of crucial information that is required to define bacterial location relationships are often neglected. Moreover, the traditional word-embedding used in previous studies may suffer from word ambiguation across linguistic contexts. RESULTS: Here, we present a deep learning model for biomedical RE. The model incorporates feature combinations of SDPs and full sentences with various attention mechanisms. We also used pre-trained contextual representations based on domain-specific vocabularies. To assess the model's robustness, we introduced a mean F1 score on many models using different random seeds. The experiments were conducted on the standard BB corpus in BioNLP-ST'16. Our experimental results revealed that the model performed better (in terms of both maximum and average F1 scores; 60.77% and 57.63%, respectively) compared with other existing models. CONCLUSIONS: We demonstrated that our proposed contributions to this task can be used to extract rich lexical, syntactic, and semantic features that effectively boost the model's performance. Moreover, we analyzed the trade-off between precision and recall to choose the proper cut-off to use in real-world applications.