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11 pages, 4229 KiB

Open AccessArticle

Attitudes towards Trusting Artificial Intelligence Insights and Factors to Prevent the Passive Adherence of GPs: A Pilot Study

by Massimo Micocci, Simone Borsci, Viral Thakerar, Simon Walne, Yasmine Manshadi, Finlay Edridge, Daniel Mullarkey, Peter Buckle and George B. Hanna

J. Clin. Med. 2021, 10(14), 3101; https://doi.org/10.3390/jcm10143101 - 14 Jul 2021

Cited by 8 | Viewed by 2656

Abstract

Artificial Intelligence (AI) systems could improve system efficiency by supporting clinicians in making appropriate referrals. However, they are imperfect by nature and misdiagnoses, if not correctly identified, can have consequences for patient care. In this paper, findings from an online survey are presented [...] Read more.

Artificial Intelligence (AI) systems could improve system efficiency by supporting clinicians in making appropriate referrals. However, they are imperfect by nature and misdiagnoses, if not correctly identified, can have consequences for patient care. In this paper, findings from an online survey are presented to understand the aptitude of GPs (n = 50) in appropriately trusting or not trusting the output of a fictitious AI-based decision support tool when assessing skin lesions, and to identify which individual characteristics could make GPs less prone to adhere to erroneous diagnostics results. The findings suggest that, when the AI was correct, the GPs’ ability to correctly diagnose a skin lesion significantly improved after receiving correct AI information, from 73.6% to 86.8% (X² (1, N = 50) = 21.787, p < 0.001), with significant effects for both the benign (X² (1, N = 50) = 21, p < 0.001) and malignant cases (X² (1, N = 50) = 4.654, p = 0.031). However, when the AI provided erroneous information, only 10% of the GPs were able to correctly disagree with the indication of the AI in terms of diagnosis (d-AIW M: 0.12, SD: 0.37), and only 14% of participants were able to correctly decide the management plan despite the AI insights (d-AIW M:0.12, SD: 0.32). The analysis of the difference between groups in terms of individual characteristics suggested that GPs with domain knowledge in dermatology were better at rejecting the wrong insights from AI. Full article

(This article belongs to the Special Issue The Convergence of Human and Artificial Intelligence on Clinical Care - Part I)

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14 pages, 1541 KiB

Open AccessArticle

Areas of Interest and Attitudes towards the Pharmacological Treatment of Attention Deficit Hyperactivity Disorder: Thematic and Quantitative Analysis Using Twitter

by Miguel Angel Alvarez-Mon, Laura de Anta, Maria Llavero-Valero, Guillermo Lahera, Miguel A. Ortega, Cesar Soutullo, Javier Quintero, Angel Asunsolo del Barco and Melchor Alvarez-Mon

J. Clin. Med. 2021, 10(12), 2668; https://doi.org/10.3390/jcm10122668 - 17 Jun 2021

Cited by 7 | Viewed by 2295

Abstract

We focused on tweets containing hashtags related to ADHD pharmacotherapy between 20 September and 31 October 2019. Tweets were classified as to whether they described medical issues or not. Tweets with medical content were classified according to the topic they referred to: side [...] Read more.

We focused on tweets containing hashtags related to ADHD pharmacotherapy between 20 September and 31 October 2019. Tweets were classified as to whether they described medical issues or not. Tweets with medical content were classified according to the topic they referred to: side effects, efficacy, or adherence. Furthermore, we classified any links included within a tweet as either scientific or non-scientific. We created a dataset of 6568 tweets: 4949 (75.4%) related to stimulants, 605 (9.2%) to non-stimulants and 1014 (15.4%) to alpha-2 agonists. Next, we manually analyzed 1810 tweets. In the end, 481 (48%) of the tweets in the stimulant group, 218 (71.9%) in the non-stimulant group and 162 (31.9%) in the alpha agonist group were considered classifiable. Stimulants accumulated the majority of tweets. Notably, the content that generated the highest frequency of tweets was that related to treatment efficacy, with alpha-2 agonist-related tweets accumulating the highest proportion of positive consideration. We found the highest percentages of tweets with scientific links in those posts related to alpha-2 agonists. Stimulant-related tweets obtained the highest proportion of likes and were the most disseminated within the Twitter community. Understanding the public view of these medications is necessary to design promotional strategies aimed at the appropriate population. Full article

(This article belongs to the Special Issue The Convergence of Human and Artificial Intelligence on Clinical Care - Part I)

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11 pages, 2936 KiB

Open AccessArticle

Usefulness of Respiratory Mechanics and Laboratory Parameter Trends as Markers of Early Treatment Success in Mechanically Ventilated Severe Coronavirus Disease: A Single-Center Pilot Study

by Daisuke Kasugai, Masayuki Ozaki, Kazuki Nishida, Hiroaki Hiraiwa, Naruhiro Jingushi, Atsushi Numaguchi, Norihito Omote, Yuichiro Shindo and Yukari Goto

J. Clin. Med. 2021, 10(11), 2513; https://doi.org/10.3390/jcm10112513 - 6 Jun 2021

Cited by 7 | Viewed by 2460

Abstract

Whether a patient with severe coronavirus disease (COVID-19) will be successfully liberated from mechanical ventilation (MV) early is important in the COVID-19 pandemic. This study aimed to characterize the time course of parameters and outcomes of severe COVID-19 in relation to the timing [...] Read more.

Whether a patient with severe coronavirus disease (COVID-19) will be successfully liberated from mechanical ventilation (MV) early is important in the COVID-19 pandemic. This study aimed to characterize the time course of parameters and outcomes of severe COVID-19 in relation to the timing of liberation from MV. This retrospective, single-center, observational study was performed using data from mechanically ventilated COVID-19 patients admitted to the ICU between 1 March 2020 and 15 December 2020. Early liberation from ventilation (EL group) was defined as successful extubation within 10 days of MV. The trends of respiratory mechanics and laboratory data were visualized and compared between the EL and prolonged MV (PMV) groups using smoothing spline and linear mixed effect models. Of 52 admitted patients, 31 mechanically ventilated COVID-19 patients were included (EL group, 20 (69%); PMV group, 11 (31%)). The patients’ median age was 71 years. While in-hospital mortality was low (6%), activities of daily living (ADL) at the time of hospital discharge were significantly impaired in the PMV group compared to the EL group (mean Barthel index (range): 30 (7.5–95) versus 2.5 (0–22.5), p = 0.048). The trends in respiratory compliance were different between patients in the EL and PMV groups. An increasing trend in the ventilatory ratio during MV until approximately 2 weeks was observed in both groups. The interaction between daily change and earlier liberation was significant in the trajectory of the thrombin–antithrombin complex, antithrombin 3, fibrinogen, C-reactive protein, lymphocyte, and positive end-expiratory pressure (PEEP) values. The indicator of physiological dead space increases during MV. The trajectory of markers of the hypercoagulation status, inflammation, and PEEP were significantly different depending on the timing of liberation from MV. These findings may provide insight into the pathophysiology of COVID-19 during treatment in the critical care setting. Full article

(This article belongs to the Special Issue The Convergence of Human and Artificial Intelligence on Clinical Care - Part I)

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11 pages, 1381 KiB

Open AccessArticle

Classification of Monocytes, Promonocytes and Monoblasts Using Deep Neural Network Models: An Area of Unmet Need in Diagnostic Hematopathology

by Mazen Osman, Zeynettin Akkus, Dragan Jevremovic, Phuong L. Nguyen, Dana Roh, Aref Al-Kali, Mrinal M. Patnaik, Ahmad Nanaa, Samia Rizk and Mohamed E. Salama

J. Clin. Med. 2021, 10(11), 2264; https://doi.org/10.3390/jcm10112264 - 24 May 2021

Cited by 4 | Viewed by 18413

Abstract

The accurate diagnosis of chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) subtypes with monocytic differentiation relies on the proper identification and quantitation of blast cells and blast-equivalent cells, including promonocytes. This distinction can be quite challenging given the cytomorphologic and immunophenotypic [...] Read more.

The accurate diagnosis of chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) subtypes with monocytic differentiation relies on the proper identification and quantitation of blast cells and blast-equivalent cells, including promonocytes. This distinction can be quite challenging given the cytomorphologic and immunophenotypic similarities among the monocytic cell precursors. The aim of this study was to assess the performance of convolutional neural networks (CNN) in separating monocytes from their precursors (i.e., promonocytes and monoblasts). We collected digital images of 935 monocytic cells that were blindly reviewed by five experienced morphologists and assigned into three subtypes: monocyte, promonocyte, and blast. The consensus between reviewers was considered as a ground truth reference label for each cell. In order to assess the performance of CNN models, we divided our data into training (70%), validation (10%), and test (20%) datasets, as well as applied fivefold cross validation. The CNN models did not perform well for predicting three monocytic subtypes, but their performance was significantly improved for two subtypes (monocyte vs. promonocytes + blasts). Our findings (1) support the concept that morphologic distinction between monocytic cells of various differentiation level is difficult; (2) suggest that combining blasts and promonocytes into a single category is desirable for improved accuracy; and (3) show that CNN models can reach accuracy comparable to human reviewers (0.78 ± 0.10 vs. 0.86 ± 0.05). As far as we know, this is the first study to separate monocytes from their precursors using CNN. Full article

(This article belongs to the Special Issue The Convergence of Human and Artificial Intelligence on Clinical Care - Part I)

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9 pages, 478 KiB

Open AccessArticle

Artificial Neural Network for Predicting the Safe Temporary Artery Occlusion Time in Intracranial Aneurysmal Surgery

by Shima Shahjouei, Seyed Mohammad Ghodsi, Morteza Zangeneh Soroush, Saeed Ansari and Shahab Kamali-Ardakani

J. Clin. Med. 2021, 10(7), 1464; https://doi.org/10.3390/jcm10071464 - 2 Apr 2021

Cited by 4 | Viewed by 1640

Abstract

Background. Temporary artery clipping facilitates safe cerebral aneurysm management, besides a risk for cerebral ischemia. We developed an artificial neural network (ANN) to predict the safe clipping time of temporary artery occlusion (TAO) during intracranial aneurysm surgery. Method. We devised a three-layer model [...] Read more.

Background. Temporary artery clipping facilitates safe cerebral aneurysm management, besides a risk for cerebral ischemia. We developed an artificial neural network (ANN) to predict the safe clipping time of temporary artery occlusion (TAO) during intracranial aneurysm surgery. Method. We devised a three-layer model to predict the safe clipping time for TAO. We considered age, the diameter of the right and left middle cerebral arteries (MCAs), the diameter of the right and left A1 segment of anterior cerebral arteries (ACAs), the diameter of the anterior communicating artery, mean velocity of flow at the right and left MCAs, and the mean velocity of flow at the right and left ACAs, as well as the Fisher grading scale of brain CT scans as the input values for the model. Results. This study included 125 patients: 105 patients from a retrospective cohort for training the model and 20 patients from a prospective cohort for validating the model. The output of the neural network yielded up to 960 s overall safe clipping time for TAO. The input values with the greatest impact on safe TAO were mean velocity of blood at left MCA and left ACA, and Fisher grading scale of brain CT scan. Conclusion. This study presents an axillary framework to improve the accuracy of the estimated safe clipping time interval of temporary artery occlusion in intracranial aneurysm surgery. Full article

(This article belongs to the Special Issue The Convergence of Human and Artificial Intelligence on Clinical Care - Part I)

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16 pages, 4187 KiB

Open AccessArticle

Prediction of Long-Term Stroke Recurrence Using Machine Learning Models

by Vida Abedi, Venkatesh Avula, Durgesh Chaudhary, Shima Shahjouei, Ayesha Khan, Christoph J Griessenauer, Jiang Li and Ramin Zand

J. Clin. Med. 2021, 10(6), 1286; https://doi.org/10.3390/jcm10061286 - 20 Mar 2021

Cited by 31 | Viewed by 5844

Abstract

Background: The long-term risk of recurrent ischemic stroke, estimated to be between 17% and 30%, cannot be reliably assessed at an individual level. Our goal was to study whether machine-learning can be trained to predict stroke recurrence and identify key clinical variables and [...] Read more.

Background: The long-term risk of recurrent ischemic stroke, estimated to be between 17% and 30%, cannot be reliably assessed at an individual level. Our goal was to study whether machine-learning can be trained to predict stroke recurrence and identify key clinical variables and assess whether performance metrics can be optimized. Methods: We used patient-level data from electronic health records, six interpretable algorithms (Logistic Regression, Extreme Gradient Boosting, Gradient Boosting Machine, Random Forest, Support Vector Machine, Decision Tree), four feature selection strategies, five prediction windows, and two sampling strategies to develop 288 models for up to 5-year stroke recurrence prediction. We further identified important clinical features and different optimization strategies. Results: We included 2091 ischemic stroke patients. Model area under the receiver operating characteristic (AUROC) curve was stable for prediction windows of 1, 2, 3, 4, and 5 years, with the highest score for the 1-year (0.79) and the lowest score for the 5-year prediction window (0.69). A total of 21 (7%) models reached an AUROC above 0.73 while 110 (38%) models reached an AUROC greater than 0.7. Among the 53 features analyzed, age, body mass index, and laboratory-based features (such as high-density lipoprotein, hemoglobin A1c, and creatinine) had the highest overall importance scores. The balance between specificity and sensitivity improved through sampling strategies. Conclusion: All of the selected six algorithms could be trained to predict the long-term stroke recurrence and laboratory-based variables were highly associated with stroke recurrence. The latter could be targeted for personalized interventions. Model performance metrics could be optimized, and models can be implemented in the same healthcare system as intelligent decision support for targeted intervention. Full article

(This article belongs to the Special Issue The Convergence of Human and Artificial Intelligence on Clinical Care - Part I)

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14 pages, 856 KiB

Open AccessArticle

SARS-CoV-2 Is a Culprit for Some, but Not All Acute Ischemic Strokes: A Report from the Multinational COVID-19 Stroke Study Group

by Shima Shahjouei, Michelle Anyaehie, Eric Koza, Georgios Tsivgoulis, Soheil Naderi, Ashkan Mowla, Venkatesh Avula, Alireza Vafaei Sadr, Durgesh Chaudhary, Ghasem Farahmand, Christoph Griessenauer, Mahmoud Reza Azarpazhooh, Debdipto Misra, Jiang Li, Vida Abedi, Ramin Zand and the Multinational COVID- Stroke Study Group

J. Clin. Med. 2021, 10(5), 931; https://doi.org/10.3390/jcm10050931 - 1 Mar 2021

Cited by 18 | Viewed by 3185

Abstract

Background. SARS-CoV-2 infected patients are suggested to have a higher incidence of thrombotic events such as acute ischemic strokes (AIS). This study aimed at exploring vascular comorbidity patterns among SARS-CoV-2 infected patients with subsequent stroke. We also investigated whether the comorbidities and their [...] Read more.

Background. SARS-CoV-2 infected patients are suggested to have a higher incidence of thrombotic events such as acute ischemic strokes (AIS). This study aimed at exploring vascular comorbidity patterns among SARS-CoV-2 infected patients with subsequent stroke. We also investigated whether the comorbidities and their frequencies under each subclass of TOAST criteria were similar to the AIS population studies prior to the pandemic. Methods. This is a report from the Multinational COVID-19 Stroke Study Group. We present an original dataset of SASR-CoV-2 infected patients who had a subsequent stroke recorded through our multicenter prospective study. In addition, we built a dataset of previously reported patients by conducting a systematic literature review. We demonstrated distinct subgroups by clinical risk scoring models and unsupervised machine learning algorithms, including hierarchical K-Means (ML-K) and Spectral clustering (ML-S). Results. This study included 323 AIS patients from 71 centers in 17 countries from the original dataset and 145 patients reported in the literature. The unsupervised clustering methods suggest a distinct cohort of patients (ML-K: 36% and ML-S: 42%) with no or few comorbidities. These patients were more than 6 years younger than other subgroups and more likely were men (ML-K: 59% and ML-S: 60%). The majority of patients in this subgroup suffered from an embolic-appearing stroke on imaging (ML-K: 83% and ML-S: 85%) and had about 50% risk of large vessel occlusions (ML-K: 50% and ML-S: 53%). In addition, there were two cohorts of patients with large-artery atherosclerosis (ML-K: 30% and ML-S: 43% of patients) and cardioembolic strokes (ML-K: 34% and ML-S: 15%) with consistent comorbidity and imaging patterns. Binominal logistic regression demonstrated that ischemic heart disease (odds ratio (OR), 4.9; 95% confidence interval (CI), 1.6–14.7), atrial fibrillation (OR, 14.0; 95% CI, 4.8–40.8), and active neoplasm (OR, 7.1; 95% CI, 1.4–36.2) were associated with cardioembolic stroke. Conclusions. Although a cohort of young and healthy men with cardioembolic and large vessel occlusions can be distinguished using both clinical sub-grouping and unsupervised clustering, stroke in other patients may be explained based on the existing comorbidities. Full article

(This article belongs to the Special Issue The Convergence of Human and Artificial Intelligence on Clinical Care - Part I)

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17 pages, 2213 KiB

Open AccessArticle

Early Detection of Septic Shock Onset Using Interpretable Machine Learners

by Debdipto Misra, Venkatesh Avula, Donna M. Wolk, Hosam A. Farag, Jiang Li, Yatin B. Mehta, Ranjeet Sandhu, Bipin Karunakaran, Shravan Kethireddy, Ramin Zand and Vida Abedi

J. Clin. Med. 2021, 10(2), 301; https://doi.org/10.3390/jcm10020301 - 15 Jan 2021

Cited by 19 | Viewed by 22119

Abstract

Background: Developing a decision support system based on advances in machine learning is one area for strategic innovation in healthcare. Predicting a patient’s progression to septic shock is an active field of translational research. The goal of this study was to develop a [...] Read more.

Background: Developing a decision support system based on advances in machine learning is one area for strategic innovation in healthcare. Predicting a patient’s progression to septic shock is an active field of translational research. The goal of this study was to develop a working model of a clinical decision support system for predicting septic shock in an acute care setting for up to 6 h from the time of admission in an integrated healthcare setting. Method: Clinical data from Electronic Health Record (EHR), at encounter level, were used to build a predictive model for progression from sepsis to septic shock up to 6 h from the time of admission; that is, T = 1, 3, and 6 h from admission. Eight different machine learning algorithms (Random Forest, XGBoost, C5.0, Decision Trees, Boosted Logistic Regression, Support Vector Machine, Logistic Regression, Regularized Logistic, and Bayes Generalized Linear Model) were used for model development. Two adaptive sampling strategies were used to address the class imbalance. Data from two sources (clinical and billing codes) were used to define the case definition (septic shock) using the Centers for Medicare & Medicaid Services (CMS) Sepsis criteria. The model assessment was performed using Area under Receiving Operator Characteristics (AUROC), sensitivity, and specificity. Model predictions for each feature window (1, 3 and 6 h from admission) were consolidated. Results: Retrospective data from April 2005 to September 2018 were extracted from the EHR, Insurance Claims, Billing, and Laboratory Systems to create a dataset for septic shock detection. The clinical criteria and billing information were used to label patients into two classes-septic shock patients and sepsis patients at three different time points from admission, creating two different case-control cohorts. Data from 45,425 unique in-patient visits were used to build 96 prediction models comparing clinical-based definition versus billing-based information as the gold standard. Of the 24 consolidated models (based on eight machine learning algorithms and three feature windows), four models reached an AUROC greater than 0.9. Overall, all the consolidated models reached an AUROC of at least 0.8820 or higher. Based on the AUROC of 0.9483, the best model was based on Random Forest, with a sensitivity of 83.9% and specificity of 88.1%. The sepsis detection window at 6 h outperformed the 1 and 3-h windows. The sepsis definition based on clinical variables had improved performance when compared to the sepsis definition based on only billing information. Conclusion: This study corroborated that machine learning models can be developed to predict septic shock using clinical and administrative data. However, the use of clinical information to define septic shock outperformed models developed based on only administrative data. Intelligent decision support tools can be developed and integrated into the EHR and improve clinical outcomes and facilitate the optimization of resources in real-time. Full article

(This article belongs to the Special Issue The Convergence of Human and Artificial Intelligence on Clinical Care - Part I)

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23 pages, 2645 KiB

Open AccessArticle

Increasing the Density of Laboratory Measures for Machine Learning Applications

by Vida Abedi, Jiang Li, Manu K. Shivakumar, Venkatesh Avula, Durgesh P. Chaudhary, Matthew J. Shellenberger, Harshit S. Khara, Yanfei Zhang, Ming Ta Michael Lee, Donna M. Wolk, Mohammed Yeasin, Raquel Hontecillas, Josep Bassaganya-Riera and Ramin Zand

J. Clin. Med. 2021, 10(1), 103; https://doi.org/10.3390/jcm10010103 - 30 Dec 2020

Cited by 10 | Viewed by 3096

Abstract

Background. The imputation of missingness is a key step in Electronic Health Records (EHR) mining, as it can significantly affect the conclusions derived from the downstream analysis in translational medicine. The missingness of laboratory values in EHR is not at random, yet imputation [...] Read more.

Background. The imputation of missingness is a key step in Electronic Health Records (EHR) mining, as it can significantly affect the conclusions derived from the downstream analysis in translational medicine. The missingness of laboratory values in EHR is not at random, yet imputation techniques tend to disregard this key distinction. Consequently, the development of an adaptive imputation strategy designed specifically for EHR is an important step in improving the data imbalance and enhancing the predictive power of modeling tools for healthcare applications. Method. We analyzed the laboratory measures derived from Geisinger’s EHR on patients in three distinct cohorts—patients tested for Clostridioides difficile (Cdiff) infection, patients with a diagnosis of inflammatory bowel disease (IBD), and patients with a diagnosis of hip or knee osteoarthritis (OA). We extracted Logical Observation Identifiers Names and Codes (LOINC) from which we excluded those with 75% or more missingness. The comorbidities, primary or secondary diagnosis, as well as active problem lists, were also extracted. The adaptive imputation strategy was designed based on a hybrid approach. The comorbidity patterns of patients were transformed into latent patterns and then clustered. Imputation was performed on a cluster of patients for each cohort independently to show the generalizability of the method. The results were compared with imputation applied to the complete dataset without incorporating the information from comorbidity patterns. Results. We analyzed a total of 67,445 patients (11,230 IBD patients, 10,000 OA patients, and 46,215 patients tested for C. difficile infection). We extracted 495 LOINC and 11,230 diagnosis codes for the IBD cohort, 8160 diagnosis codes for the Cdiff cohort, and 2042 diagnosis codes for the OA cohort based on the primary/secondary diagnosis and active problem list in the EHR. Overall, the most improvement from this strategy was observed when the laboratory measures had a higher level of missingness. The best root mean square error (RMSE) difference for each dataset was recorded as −35.5 for the Cdiff, −8.3 for the IBD, and −11.3 for the OA dataset. Conclusions. An adaptive imputation strategy designed specifically for EHR that uses complementary information from the clinical profile of the patient can be used to improve the imputation of missing laboratory values, especially when laboratory codes with high levels of missingness are included in the analysis. Full article

(This article belongs to the Special Issue The Convergence of Human and Artificial Intelligence on Clinical Care - Part I)

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15 pages, 2987 KiB

Open AccessArticle

Convolutional Neural Network Classifies Pathological Voice Change in Laryngeal Cancer with High Accuracy

by HyunBum Kim, Juhyeong Jeon, Yeon Jae Han, YoungHoon Joo, Jonghwan Lee, Seungchul Lee and Sun Im

J. Clin. Med. 2020, 9(11), 3415; https://doi.org/10.3390/jcm9113415 - 25 Oct 2020

Cited by 44 | Viewed by 5937

Abstract

Voice changes may be the earliest signs in laryngeal cancer. We investigated whether automated voice signal analysis can be used to distinguish patients with laryngeal cancer from healthy subjects. We extracted features using the software package for speech analysis in phonetics (PRAAT) and [...] Read more.

Voice changes may be the earliest signs in laryngeal cancer. We investigated whether automated voice signal analysis can be used to distinguish patients with laryngeal cancer from healthy subjects. We extracted features using the software package for speech analysis in phonetics (PRAAT) and calculated the Mel-frequency cepstral coefficients (MFCCs) from voice samples of a vowel sound of /a:/. The proposed method was tested with six algorithms: support vector machine (SVM), extreme gradient boosting (XGBoost), light gradient boosted machine (LGBM), artificial neural network (ANN), one-dimensional convolutional neural network (1D-CNN) and two-dimensional convolutional neural network (2D-CNN). Their performances were evaluated in terms of accuracy, sensitivity, and specificity. The result was compared with human performance. A total of four volunteers, two of whom were trained laryngologists, rated the same files. The 1D-CNN showed the highest accuracy of 85% and sensitivity and sensitivity and specificity levels of 78% and 93%. The two laryngologists achieved accuracy of 69.9% but sensitivity levels of 44%. Automated analysis of voice signals could differentiate subjects with laryngeal cancer from those of healthy subjects with higher diagnostic properties than those performed by the four volunteers. Full article

(This article belongs to the Special Issue The Convergence of Human and Artificial Intelligence on Clinical Care - Part I)

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12 pages, 1615 KiB

Open AccessArticle

Using Bayesian Networks to Predict Long-Term Health-Related Quality of Life and Comorbidity after Bariatric Surgery: A Study Based on the Scandinavian Obesity Surgery Registry

by Yang Cao, Mustafa Raoof, Eva Szabo, Johan Ottosson and Ingmar Näslund

J. Clin. Med. 2020, 9(6), 1895; https://doi.org/10.3390/jcm9061895 - 17 Jun 2020

Cited by 12 | Viewed by 3287

Abstract

Previously published literature has identified a few predictors of health-related quality of life (HRQoL) after bariatric surgery. However, performance of the predictive models was not evaluated rigorously using real world data. To find better methods for predicting prognosis in patients after bariatric surgery, [...] Read more.

Previously published literature has identified a few predictors of health-related quality of life (HRQoL) after bariatric surgery. However, performance of the predictive models was not evaluated rigorously using real world data. To find better methods for predicting prognosis in patients after bariatric surgery, we examined performance of the Bayesian networks (BN) method in predicting long-term postoperative HRQoL and compared it with the convolution neural network (CNN) and multivariable logistic regression (MLR). The patients registered in the Scandinavian Obesity Surgery Registry (SOReg) were used for the current study. In total, 6542 patients registered in the SOReg between 2008 and 2012 with complete demographic and preoperative comorbidity information, and preoperative and postoperative 5-year HROoL scores and comorbidities were included in the study. HRQoL was measured using the RAND-SF-36 and the obesity-related problems scale. Thirty-five variables were used for analyses, including 19 predictors and 16 outcome variables. The Gaussian BN (GBN), CNN, and a traditional linear regression model were used for predicting 5-year HRQoL scores, and multinomial discrete BN (DBN) and MLR were used for 5-year comorbidities. Eighty percent of the patients were randomly selected as a training dataset and 20% as a validation dataset. The GBN presented a better performance than the CNN and the linear regression model; it had smaller mean squared errors (MSEs) than those from the CNN and the linear regression model. The MSE of the summary physical scale was only 0.0196 for GBN compared to the 0.0333 seen in the CNN. The DBN showed excellent predictive ability for 5-year type 2 diabetes and dyslipidemia (area under curve (AUC) = 0.942 and 0.917, respectively), good ability for 5-year hypertension and sleep apnea syndrome (AUC = 0.891 and 0.834, respectively), and fair ability for 5-year depression (AUC = 0.750). Bayesian networks provide useful tools for predicting long-term HRQoL and comorbidities in patients after bariatric surgery. The hybrid network that may involve variables from different probability distribution families deserves investigation in the future. Full article

(This article belongs to the Special Issue The Convergence of Human and Artificial Intelligence on Clinical Care - Part I)

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Review

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16 pages, 693 KiB

Open AccessReview

Artificial Intelligence (AI)-Empowered Echocardiography Interpretation: A State-of-the-Art Review

by Zeynettin Akkus, Yousof H. Aly, Itzhak Z. Attia, Francisco Lopez-Jimenez, Adelaide M. Arruda-Olson, Patricia A. Pellikka, Sorin V. Pislaru, Garvan C. Kane, Paul A. Friedman and Jae K. Oh

J. Clin. Med. 2021, 10(7), 1391; https://doi.org/10.3390/jcm10071391 - 30 Mar 2021

Cited by 32 | Viewed by 6598

Abstract

Echocardiography (Echo), a widely available, noninvasive, and portable bedside imaging tool, is the most frequently used imaging modality in assessing cardiac anatomy and function in clinical practice. On the other hand, its operator dependability introduces variability in image acquisition, measurements, and interpretation. To [...] Read more.

Echocardiography (Echo), a widely available, noninvasive, and portable bedside imaging tool, is the most frequently used imaging modality in assessing cardiac anatomy and function in clinical practice. On the other hand, its operator dependability introduces variability in image acquisition, measurements, and interpretation. To reduce these variabilities, there is an increasing demand for an operator- and interpreter-independent Echo system empowered with artificial intelligence (AI), which has been incorporated into diverse areas of clinical medicine. Recent advances in AI applications in computer vision have enabled us to identify conceptual and complex imaging features with the self-learning ability of AI models and efficient parallel computing power. This has resulted in vast opportunities such as providing AI models that are robust to variations with generalizability for instantaneous image quality control, aiding in the acquisition of optimal images and diagnosis of complex diseases, and improving the clinical workflow of cardiac ultrasound. In this review, we provide a state-of-the art overview of AI-empowered Echo applications in cardiology and future trends for AI-powered Echo technology that standardize measurements, aid physicians in diagnosing cardiac diseases, optimize Echo workflow in clinics, and ultimately, reduce healthcare costs. Full article

(This article belongs to the Special Issue The Convergence of Human and Artificial Intelligence on Clinical Care - Part I)

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