1,721,738 research outputs found
Upper Aero Digestive Tract Cancer Diagnosis using Deep Learning Methods
No full textObjective: Narrow band imaging (NBI) and white light (WL) are endoscopic techniques to visualize upper aero digestive tract (UADT) cancers. However, these imaging techniques are less effective for diagnosing tumors in less competent centers since they depend on skilled medical experts. Recently, there has been evidence that deep learning (DL) has potential applications in UADT video endoscopy. This research aims to develop a DL for the automatic identification and delineation of UADT cancer.
Approach: In both WL and NBI frames, the YOLO DL model (YOLOv5s with YOLOv5m) ensemble, was used to diagnose laryngeal squamous cell carcinoma (LSCC). Six external LSCC laryngoscopy videos were tested in real-time for cancer detection. The SegMENT is a segmentation convolution neural networks (CNN), model proposed based on a modified DeepLabV3+ model for precise UADT delineation using an in-domain transfer learning ensemble technique. Its accuracy was further validated on external datasets with NBI images of oral cavity SCC (OSCC) and oropharyngeal SCC (OPSCC). The SegMENT-Plus is the improved version of SegMENT model designed for large LSCC datasets. SegMENT-Plus used EfficientNetB5 backbone as an encoder with a modified atrous spatial pyramid pooling (m-ASPP) block. The attentions blocks (SE and CBAM) were integrated into m-ASPP module to improve cancer segmentation. The m-ASPP was used to extract local and global LSCC features to overcome the limitation of conventional ASPP modules in literature. SegMENT-Plus was evaluated using a multi-center dataset from three hospitals (Genoa, Brescia, Seoul South Korea). The model was tested on LSCC frames, the delineation performance was compared with three otolaryngology experts. The unseen intraoperative laryngoscopy videos also validated for real-time performance. The SegMENT-Plus was compared with its predecessor SegMENT and other DL models (UNET, ResUNET, DeepLabv3+, DoubleUET,).
Main results: In the LSCC detection task, 219 patients from Genoa, Italy were enrolled, and were provided 624 LSCC video frames. YOLO models were trained using an 82.6% training set, an 8.2% validation set, and a 9.2% testing set. The ensemble algorithm (YOLOv5s with YOLOv5m —Test Time Augmentation) achieved top LSCC detection with 66% Precision, 62% Recall, and 63% mean Average Precision at 0.5 intersection over union (IoU). The average computation time per frame on laryngoscopy videos was 0.026 seconds. The SegMENT model for the UADT cancer delineation was developed using 219 patients (624 larynx frames), and external validation from Brescia, Italy for the OPSCC and OCSCC cohorts involved 116 and 102 NBI images, respectively. The SegMENT model achieved 0.68% IoU and 0.81% dice coefficient (DSC), outperforming other DL models. The DSC values in the OCSCC and OPSCC datasets improved significantly, with median DSC values of 10.3% and 11.9%, respectively. This study includes 557 patients with 3933 laryngeal images from Genoa, Italy to the development of SegMENT-Plus to improve LDCC delineation. The optimal performance and generalization of the algorithm were confirmed by external testing cohorts from Seoul, South Korea, and Brescia, Italy. The external cohorts showed DSC between 81.4% and 84.9% and IoU between 81.8% and 85.7%.
Significance: The study identified a suitable CNN model for LSCC detection in WL and NBI video laryngoscopes. SegMENT outperformed previous results in external validation cohorts, showing promise for precise tumor segmentation. SegMENT-Plus holds the potential for improved early tumor detection and delineation, laying the foundation for a clinical system in LSCC margin delineation
Hypermobility, ACL reconstruction & shoulder instability: a clinical, mechanical and histological analysis
Full text linkJoint movements are essential for the function of human body during the activities of daily
living and sports. The movement of human joints varies from normal to those which have an
increased range of joint movement (gymnasts) to those with extreme disabling laxity in patients
with a connective tissue disorder (Ehlers Danlos Syndrome).
“Hypermobility" is most commonly used to describe excessive movement. Hypermobility was
assessed by using the current criteria of the Beighton score for signs and the Brighton criteria for
symptoms of hypermobility in a group of orthopaedic patients attending the specialist knee and
shoulder injury clinics.
The Beighton score was found to be higher in patients attending for primary ACL reconstruction
(mean 2.9, p = 0.002) and revision ACL reconstruction (mean 4, p < 0.001) when compared
with the control group. Hypermobility was a risk factor for the failure of ACL reconstruction
(30% vs 0%). The mean Beighton score was higher in both the primary shoulder dislocation
group (mean difference 1.8, p=0.001) and the recurrent shoulder dislocation group (mean
difference 1.4, p=0.004). Bone defects were studied on the CT scan following shoulder
dislocations. There was no correlation between hypermobility and the bone defects. The bone
defect was a risk factor for recurrent shoulder instability (48% vs 16%).
A material testing system was used to assess the tissue laxity of discarded hamstring tendon and
shoulder capsule obtained during stabilisation procedures. The mean gradient of slope for both
tendon and capsule graphs was 23.8 (range 3.08-52.63). The tissue laxity was compared to the
Beighton score, however no correlation was detected between the Beighton score and the
gradient of the tissue laxity.
An electronic goniometer was used to measure the angle of the MCP joint of the little finger,
whilst a force plate system simultaneously measured the force required to hyperextend the MCP
joint. The little finger MCP joints of each hand were assessed in this manner in a group of
patients undergoing primary ACL reconstruction or open shoulder stabilization. The mean force
required to produce the 40 degrees angle at the little finger MCP joint was 0.04 kg with a range
from 0-0.11 kg. There was a positive correlation between the gradient of tissue laxity and the
force required to produce 40 degrees angle at the little finger of the dominant hand.
The expression of Collagen V and Small leucine rich proteoglycans (Decorin and Biglycan) was
studied in the skin, hamstring tendon and shoulder capsule of the patients described above
attending with shoulder or knee instability. These patients had different levels of hypermobility
(as assessed by the Beighton score) and symptoms of hypermobility (as assessed by the
Brighton criteria to diagnose Benign Joint Hypermobility Syndrome). The weaker tendon group
was found to have a lower mean Beighton score, while the weaker skin group had a higher mean
Beighton score.
Collagen V expression was higher in the skin dermal papillae of the weaker group.
The Beighton Scores were higher in patients with ACL and shoulder injuries. Hypermobility
was a risk factor for the failure of ACL reconstruction. There was no correlation between
hypermobility and the bone defects on the CT scan following shoulder dislocation. Bone defects
were a risk factor for recurrence. There was no correlation between the Beighton Score and the
tissue laxity. There was a correlation between the tissue laxity and the clinical assessment of
laxity at the little finger MCPJ by using a force- goniometer system. There was a correlation
between the collagen V expression in the dermal papillae of the skin and the Beighton score
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Artificial Intelligence for Upper Aerodigestive Tract Endoscopy and Laryngoscopy: A Guide for Physicians and State‐of‐the‐Art Review
No full textObjective: The endoscopic and laryngoscopic examination is paramount for laryngeal, oropharyngeal, nasopharyngeal, nasal, and oral cavity benign lesions and cancer evaluation. Nevertheless, upper aerodigestive tract (UADT) endoscopy is intrinsically operator-dependent and lacks objective quality standards. At present, there has been an increased interest in artificial intelligence (AI) applications in this area to support physicians during the examination, thus enhancing diagnostic performances. The relative novelty of this research field poses a challenge both for the reviewers and readers as clinicians often lack a specific technical background. Data sources: Four bibliographic databases were searched: PubMed, EMBASE, Cochrane, and Google Scholar. Review methods: A structured review of the current literature (up to September 2022) was performed. Search terms related to topics of AI, machine learning (ML), and deep learning (DL) in UADT endoscopy and laryngoscopy were identified and queried by 3 independent reviewers. Citations of selected studies were also evaluated to ensure comprehensiveness. Conclusions: Forty-one studies were included in the review. AI and computer vision techniques were used to achieve 3 fundamental tasks in this field: classification, detection, and segmentation. All papers were summarized and reviewed. Implications for practice: This article comprehensively reviews the latest developments in the application of ML and DL in UADT endoscopy and laryngoscopy, as well as their future clinical implications. The technical basis of AI is also explained, providing guidance for nonexpert readers to allow critical appraisal of the evaluation metrics and the most relevant quality requirements
Trait and State Anxiety Effects on Mismatch Negativity and Sensory Gating Event-Related Potentials
Full text linkWe used the auditory roving oddball to investigate whether individual differences in self-reported anxiety influence event-related potential (ERP) activity related to sensory gating and mismatch negativity (MMN). The state-trait anxiety inventory (STAI) was used to assess the effects of anxiety on the ERPs for auditory change detection and information filtering in a sample of thirty-six healthy participants. The roving oddball paradigm involves presentation of stimulus trains of auditory tones with certain frequencies followed by trains of tones with different frequencies. Enhanced negative mid-latency response (130–230 ms post-stimulus) was marked at the deviant (first tone) and the standard (six or more repetitions) tone at Fz, indicating successful mismatch negativity (MMN). In turn, the first and second tone in a stimulus train were subject to sensory gating at the Cz electrode site as a response to the second stimulus was suppressed at an earlier latency (40–80 ms). We used partial correlations and analyses of covariance to investigate the influence of state and trait anxiety on these two processes. Higher trait anxiety exhibited enhanced MMN amplitude (more negative) (F(1,33) = 14.259, p = 6.323 × 10−6, ηp2 = 0.302), whereas state anxiety reduced sensory gating (F(1,30) = 13.117, p = 0.001, ηp2 = 0.304). Our findings suggest that high trait-anxious participants demonstrate hypervigilant change detection to deviant tones that appear more salient, whereas increased state anxiety associates with failure to filter out irrelevant stimuli
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
- …
