scholarly journals Multispectral Imaging Algorithm Predicts Breslow Thickness of Melanoma

2021 ◽  
Vol 11 (1) ◽  
pp. 189
Author(s):  
Szabolcs Bozsányi ◽  
Noémi Nóra Varga ◽  
Klára Farkas ◽  
András Bánvölgyi ◽  
Kende Lőrincz ◽  
...  
Keyword(s):  
Multispectral Imaging ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Breslow Thickness ◽  
Substantial Agreement ◽  
Quantitative Parameter ◽  
Clinical Images ◽  
Melanoma Diagnosis ◽  
Safety Margins ◽  
Novel Method

Breslow thickness is a major prognostic factor for melanoma. It is based on histopathological evaluation, and thus it is not available to aid clinical decision making at the time of the initial melanoma diagnosis. In this work, we assessed the efficacy of multispectral imaging (MSI) to predict Breslow thickness and developed a classification algorithm to determine optimal safety margins of the melanoma excision. First, we excluded nevi from the analysis with a novel quantitative parameter. Parameter s’ could differentiate nevi from melanomas with a sensitivity of 89.60% and specificity of 88.11%. Following this step, we have categorized melanomas into three different subgroups based on Breslow thickness (≤1 mm, 1–2 mm and >2 mm) with a sensitivity of 78.00% and specificity of 89.00% and a substantial agreement (κ = 0.67; 95% CI, 0.58–0.76). We compared our results to the performance of dermatologists and dermatology residents who assessed dermoscopic and clinical images of these melanomas, and reached a sensitivity of 60.38% and specificity of 80.86% with a moderate agreement (κ = 0.41; 95% CI, 0.39–0.43). Based on our findings, this novel method may help predict the appropriate safety margins for curative melanoma excision.

Clinical Conundrums When Integrating the QbTest into a Standard ADHD Assessment of Children and Young People

2021 ◽  
Author(s):  
Carsten Vogt
Keyword(s):  
Clinical Practice ◽  
Clinical Decision Making ◽  
Ecological Validity ◽  
Neuropsychological Test ◽  
Real Life ◽  
Clinical Decision ◽  
Hyperactivity Disorder ◽  
Standard Clinical Practice ◽  
Novel Method ◽  
The Impact

AbstractThe uptake of the QbTest in clinical practice is increasing and has recently been supported by research evidence proposing its effectiveness in relation to clinical decision-making. However, the exact underlying process leading to this clinical benefit is currently not well established and requires further clarification. For the clinician, certain challenges arise when adding the QbTest as a novel method to standard clinical practice, such as having the skills required to interpret neuropsychological test information and assess for diagnostically relevant neurocognitive domains that are related to attention-deficit hyperactivity disorder (ADHD), or how neurocognitive domains express themselves within the behavioral classifications of ADHD and how the quantitative measurement of activity in a laboratory setting compares with real-life (ecological validity) situations as well as the impact of comorbidity on test results. This article aims to address these clinical conundrums in aid of developing a consistent approach and future guidelines in clinical practice.

scholarly journals Artificial Intelligence to Generate Medical Images: Augmenting the Cardiologist’s Visual Clinical Workflow

2021 ◽  
Author(s):  
Max L Olender ◽  
José M de la Torre Hernández ◽  
Lambros S Athanasiou ◽  
Farhad R Nezami ◽  
Elazer R Edelman
Keyword(s):  
Artificial Intelligence ◽  
Clinical Decision Making ◽  
Digital Health ◽  
Clinical Decision ◽  
Great Promise ◽  
Plaque Morphology ◽  
Ultrasound Images ◽  
Clinical Workflow ◽  
Generative Adversarial Network ◽  
Clinical Images

Abstract Artificial intelligence (AI) offers great promise in cardiology, and medicine broadly, for its ability to tirelessly integrate vast amounts of data. Applications in medical imaging are particularly attractive, as images are a powerful means to convey rich information and are extensively utilized in cardiology practice. Departing from other AI approaches in cardiology focused on task automation and pattern recognition, we describe a digital health platform to synthesize enhanced, yet familiar, clinical images to augment the cardiologist’s visual clinical workflow. In this article, we present the framework, technical fundamentals, and functional applications of the methodology, especially as it pertains to intravascular imaging. A conditional generative adversarial network was trained with annotated images of atherosclerotic diseased arteries to generate synthetic optical coherence tomography and intravascular ultrasound images on the basis of specified plaque morphology. Systems leveraging this unique and flexible construct, whereby a pair of neural networks are competitively trained in tandem, can rapidly generate useful images. These synthetic images replicate the style, and in several ways exceed the content and function, of normally acquired images. By using this technique and employing AI in such applications, one can ameliorate challenges in image quality, interpretability, coherence, completeness, and granularity, thereby enhancing medical education and clinical decision-making.

scholarly journals An extensible big data software architecture managing a research resource of real-world clinical radiology data linked to other health data from the whole Scottish population

GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
Author(s):  
Thomas Nind ◽  
James Sutherland ◽  
Gordon McAllister ◽  
Douglas Hardy ◽  
Ally Hume ◽  
...  
Keyword(s):  
Real World ◽  
Clinical Decision Making ◽  
Clinical Care ◽  
Personal Data ◽  
Large Data ◽  
Clinical Decision ◽  
Learning Technologies ◽  
Training Algorithms ◽  
Clinical Images ◽  
Radiological Data

Abstract Aim To enable a world-leading research dataset of routinely collected clinical images linked to other routinely collected data from the whole Scottish national population. This includes more than 30 million different radiological examinations from a population of 5.4 million and >2 PB of data collected since 2010. Methods Scotland has a central archive of radiological data used to directly provide clinical care to patients. We have developed an architecture and platform to securely extract a copy of those data, link it to other clinical or social datasets, remove personal data to protect privacy, and make the resulting data available to researchers in a controlled Safe Haven environment. Results An extensive software platform has been developed to host, extract, and link data from cohorts to answer research questions. The platform has been tested on 5 different test cases and is currently being further enhanced to support 3 exemplar research projects. Conclusions The data available are from a range of radiological modalities and scanner types and were collected under different environmental conditions. These real-world, heterogenous data are valuable for training algorithms to support clinical decision making, especially for deep learning where large data volumes are required. The resource is now available for international research access. The platform and data can support new health research using artificial intelligence and machine learning technologies, as well as enabling discovery science.

scholarly journals The suitability of panoramic radiographs for clinical decision making regarding root angulation compared to cone-beam computed tomography

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Athbi Alqareer ◽  
Rania Nada ◽  
Aref Ghayyath ◽  
Mariam Baghdady ◽  
Veerasathpurush Allareddy
Keyword(s):  
Computed Tomography ◽  
Cone Beam Computed Tomography ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Cone Beam ◽  
Clinical Decisions ◽  
Substantial Agreement ◽  
Statistical Reliability ◽  
Blinded Manner ◽  
Root Proximity

Abstract Background The study compared clinical decisions regarding root angulation correction and root proximity based on the interpretation of Panoramic (PAN) versus Cone-Beam Computed Tomography (CBCT) images. Methods A total of 864 teeth from 36 existing, radiographic patient records at a university dental clinic with concurrent PAN and CBCT images were assessed using PANs, then using CBCTs in a blinded manner by two orthodontists. Teeth were rated regarding the need for root repositioning, the direction of repositioning and existence of root proximity. Frequencies, rating time and intra- and inter-examiner Cohen’s Kappa were calculated. Results There was 73.7–84.5% agreement between PAN-based and CBCT-based orthodontists’ decisions regarding the need to reposition roots. Root proximity was more frequently reported on PANs than CBCTs by one examiner (p = 0.001 and p = 0.168). Both PANs and CBCTs had moderate to substantial intra-examiner, within-radiograph-type reliability with Kappa values of 0.686–0.79 for PANs, and 0.661 for CBCTs (p < 0.001). Inter-examiner and inter-radiograph-type Kappa values ranged from 0.414 to 0.51 (p < 0.001). Using CBCT decisions as a reference, 78.9% of PAN decisions were coincident, 9.3% would have been repositioned on CBCT but not on PAN, 11.3% would not have been repositioned on CBCT but were on PAN, and 0.3% would have been repositioned in the opposite direction on CBCT versus PAN. Additionally, CBCT images required more time per tooth to assess than PANs (p < 0.001). Conclusions PAN-based clinical decisions regarding root angulation had comparable statistical reliability and substantial agreement with CBCT-based clinical decisions.

scholarly journals NCMHap: A novel method for haplotype reconstruction based on Neutrosophic c-means clustering

2020 ◽  
Author(s):  
Fatemeh Zamani ◽  
mohammad Olyaee ◽  
Alireza Khanteymoori
Keyword(s):  
Clustering Algorithm ◽  
Clinical Decision Making ◽  
Genetic Diseases ◽  
Clinical Decision ◽  
Single Individual ◽  
Haplotype Reconstruction ◽  
Huge Amount ◽  
Noisy Input ◽  
Benchmark Datasets ◽  
Novel Method

Abstract Background: Single individual haplotype (SIH) problem refers to reconstructing haplotypes of an individual based on several input fragments sequenced from a specified chromosome. Solving this problem is an important task in computational biology and has many applications in the pharmaceutical industry, clinical decision-making, and genetic diseases. It is known that solving the problem is NP-hard. Although several methods have been proposed to solve the problem, it is found that most of them have low performances in dealing with noisy input fragments. Therefore, proposing a method which is accurate and scalable, is a challenging task. Results: In this paper, we introduced a method, named NCMHap, which utilizes the Neutrosophic c-means (NCM) clustering algorithm. The NCM algorithm can effectively detect the noise and outliers in the input data. In addition, it can reduce their effects in the clustering process. The proposed method has been evaluated by several benchmark datasets. Comparing with existing methods indicates when NCM is tuned by suitable parameters, the results are encouraging. In particular, when the amount of noise increases, it outperforms the comparing methods. Conclusion: The proposed method is validated using simulated and real datasets. The achieved results recommend the application of NCMHap on the datasets which involve the fragments with a huge amount of gaps and noise.

scholarly journals NCMHap: A novel method for haplotype reconstruction based on Neutrosophic c-means clustering

2020 ◽  
Author(s):  
Fatemeh Zamani ◽  
Mohammad Olyaee ◽  
Alireza Khanteymoori
Keyword(s):  
Clustering Algorithm ◽  
Clinical Decision Making ◽  
Genetic Diseases ◽  
Clinical Decision ◽  
Single Individual ◽  
Haplotype Reconstruction ◽  
Huge Amount ◽  
Noisy Input ◽  
Benchmark Datasets ◽  
Novel Method

Abstract Background: Single individual haplotype (SIH) problem refers to reconstructing haplotypes of an individual based on several input fragments sequenced from a specified chromosome. Solving this problem is an important task in computational biology and has many applications in the pharmaceutical industry, clinical decision-making and genetic diseases. It is known that solving the problem is NP-hard. Although several methods have been proposed to solve the problem, but it is found that most of them have low performances in dealing with noisy input fragments. Therefore, proposing a method which be accurate and scalable, is a challenging task.Results: In this paper, we introduced a method, named NCMHap, which utilizes the Neutrosophic c-means (NCM) clustering algorithm. The NCM algorithm can effectively detect the noise and outliers in the input data. In addition, it can reduce their effects in the clustering process. The proposed method has been evaluated by several benchmark datasets. Comparing with existing methods indicates that NCMHap is significantly superior in the most cases, particularly when the amount of noise increases, it outperforms the comparing methods.Conclusion: The experimental results recommend the application of the proposed method on the datasets which involve the fragments with huge amount of gaps and noise.

scholarly journals NCMHap: a novel method for haplotype reconstruction based on Neutrosophic c-means clustering

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Fatemeh Zamani ◽  
Mohammad Hossein Olyaee ◽  
Alireza Khanteymoori
Keyword(s):  
Clustering Algorithm ◽  
Clinical Decision Making ◽  
Genetic Diseases ◽  
Clinical Decision ◽  
Single Individual ◽  
Haplotype Reconstruction ◽  
Huge Amount ◽  
Noisy Input ◽  
Benchmark Datasets ◽  
Novel Method

Abstract Background Single individual haplotype problem refers to reconstructing haplotypes of an individual based on several input fragments sequenced from a specified chromosome. Solving this problem is an important task in computational biology and has many applications in the pharmaceutical industry, clinical decision-making, and genetic diseases. It is known that solving the problem is NP-hard. Although several methods have been proposed to solve the problem, it is found that most of them have low performances in dealing with noisy input fragments. Therefore, proposing a method which is accurate and scalable, is a challenging task. Results In this paper, we introduced a method, named NCMHap, which utilizes the Neutrosophic c-means (NCM) clustering algorithm. The NCM algorithm can effectively detect the noise and outliers in the input data. In addition, it can reduce their effects in the clustering process. The proposed method has been evaluated by several benchmark datasets. Comparing with existing methods indicates when NCM is tuned by suitable parameters, the results are encouraging. In particular, when the amount of noise increases, it outperforms the comparing methods. Conclusion The proposed method is validated using simulated and real datasets. The achieved results recommend the application of NCMHap on the datasets which involve the fragments with a huge amount of gaps and noise.

Preliminary Findings on the Use of Session Questionnaires to Improve Clinician-Client Alliance With Special Consideration to Clinical Education

2015 ◽  
Vol 25 (1) ◽  
pp. 50-60
Author(s):  
Anu Subramanian
Keyword(s):  
Graduate Students ◽  
Clinical Education ◽  
Clinical Decision Making ◽  
Rating Scale ◽  
Treatment Effectiveness ◽  
Clinical Decision ◽  
Added Value ◽  
Therapeutic Outcomes ◽  
Specific Feedback ◽  
Stakeholder Perspective

ASHA's focus on evidence-based practice (EBP) includes the family/stakeholder perspective as an important tenet in clinical decision making. The common factors model for treatment effectiveness postulates that clinician-client alliance positively impacts therapeutic outcomes and may be the most important factor for success. One strategy to improve alliance between a client and clinician is the use of outcome questionnaires. In the current study, eight parents of toddlers who attended therapy sessions at a university clinic responded to a session outcome questionnaire that included both rating scale and descriptive questions. Six graduate students completed a survey that included a question about the utility of the questionnaire. Results indicated that the descriptive questions added value and information compared to using only the rating scale. The students were varied in their responses regarding the effectiveness of the questionnaire to increase their comfort with parents. Information gathered from the questionnaire allowed for specific feedback to graduate students to change behaviors and created opportunities for general discussions regarding effective therapy techniques. In addition, the responses generated conversations between the client and clinician focused on clients' concerns. Involving the stakeholder in identifying both effective and ineffective aspects of therapy has advantages for clinical practice and education.

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