In Revista espanola de cardiologia (English ed.)

Baladrón Carlos, Gómez de Diego José Juan, Amat-Santos Ignacio J

2020-Sep-29

Artificial intelligence, Big data, Inteligencia artificial, Medicina de sistemas, Systems medicine, mHealth

In Radiography (London, England : 1995)

OBJECTIVE : To review the available literature concerning the effectiveness of the COVID-19 diagnostic tools.

BACKGROUND : With the absence of specific treatment/vaccines for the coronavirus COVID-19, the most appropriate approach to control this infection is to quarantine people and isolate symptomatic people and suspected or infected cases. Although real-time reverse transcription-polymerase chain reaction (RT-PCR) assay is considered the first tool to make a definitive diagnosis of COVID-19 disease, the high false negative rate, low sensitivity, limited supplies and strict requirements for laboratory settings might delay accurate diagnosis. Computed tomography (CT) has been reported as an important tool to identify and investigate suspected patients with COVID-19 disease at early stage.

KEY FINDINGS : RT-PCR shows low sensitivity (60-71%) in diagnosing patients with COVID-19 infection compared to the CT chest. Several studies reported that chest CT scans show typical imaging features in all patients with COVID-19. This high sensitivity and initial presentation in CT chest can be helpful in rectifying false negative results obtained from RT-PCR. As COVID-19 has similar manifestations to other pneumonia diseases, artificial intelligence (AI) might help radiologists to differentiate COVID-19 from other pneumonia diseases.

CONCLUSION : Although CT scan is a powerful tool in COVID-19 diagnosis, it is not sufficient to detect COVID-19 alone due to the low specificity (25%), and challenges that radiologists might face in differentiating COVID-19 from other viral pneumonia on chest CT scans. AI might help radiologists to differentiate COVID-19 from other pneumonia diseases.

IMPLICATION FOR PRACTICE : Both RT-PCR and CT tests together would increase sensitivity and improve quarantine efficacy, an impact neither could achieve alone.

Alsharif W, Qurashi A

2020-Sep-21

Artificial intelligence, CT scan, Consolidation, Crazy-paving, Ground-glass opacification, RT-PCR

In Human genomics

Ahmed Zeeshan

2020-Oct-02

Artificial intelligence, Clinics, Genomics, Integrative analysis, Machine learning, Metabolomics, Precision medicine

In Journal of translational medicine

BACKGROUND : It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time.

METHODS : In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease.

RESULTS : The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II.

CONCLUSION : Results show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen.

Fahimian Ghazale, Zahiri Javad, Arab Seyed Shahriar, Sajedi Reza H

2020-Oct-02

Biological network, Breast cancer, Drug repositioning, Drug-diseases interaction, Machine learning, Network integration

In Journal of neuroinflammation

BACKGROUND : Growing evidence has shown that alterations in the gut microbiota composition were associated with a variety of neuropsychiatric conditions. However, whether such associations reflect causality remains unknown. We aimed to reveal the causal relationships among gut microbiota, metabolites, and neuropsychiatric disorders including Alzheimer's disease (AD), major depressive disorder (MDD), and schizophrenia (SCZ).

METHODS : A two-sample bi-directional Mendelian randomization analysis was performed by using genetic variants from genome-wide association studies as instrumental variables for gut microbiota, metabolites, AD, MDD, and SCZ, respectively.

RESULTS : We found suggestive associations of host-genetic-driven increase in Blautia (OR, 0.88; 95%CI, 0.79-0.99; P = 0.028) and elevated γ-aminobutyric acid (GABA) (0.96; 0.92-1.00; P = 0.034), a downstream product of Blautia-dependent arginine metabolism, with a lower risk of AD. Genetically increased Enterobacteriaceae family and Enterobacteriales order were potentially associated with a higher risk of SCZ (1.09; 1.00-1.18; P = 0.048), while Gammaproteobacteria class (0.90; 0.83-0.98; P = 0.011) was related to a lower risk for SCZ. Gut production of serotonin was potentially associated with an increased risk of SCZ (1.07; 1.00-1.15; P = 0.047). Furthermore, genetically increased Bacilli class was related to a higher risk of MDD (1.07; 1.02-1.12; P = 0.010). In the other direction, neuropsychiatric disorders altered gut microbiota composition.

CONCLUSIONS : These data for the first time provide evidence of potential causal links between gut microbiome and AD, MDD, and SCZ. GABA and serotonin may play an important role in gut microbiota-host crosstalk in AD and SCZ, respectively. Further investigations in understanding the underlying mechanisms of associations between gut microbiota and AD, MDD, and SCZ are required.

Zhuang Zhenhuang, Yang Ruotong, Wang Wenxiu, Qi Lu, Huang Tao

2020-Oct-02

Causality, Genetic association, Gut microbiota, Mendelian randomization, Neuropsychiatric disorder

In BMC medical informatics and decision making ; h5-index 38.0

BACKGROUND : Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients' disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and to supplement their own expertise. Yet, CAD systems might be based on black box machine learning models and high dimensional data sources such as electronic health records, magnetic resonance imaging scans, cardiotocograms, etc. These foundations make interpretation and explanation of the CAD advice very challenging. This challenge is recognised throughout the machine learning research community. eXplainable Artificial Intelligence (XAI) is emerging as one of the most important research areas of recent years because it addresses the interpretability and trust concerns of critical decision makers, including those in clinical and medical practice.

METHODS : In this work, we focus on AdaBoost, a black box model that has been widely adopted in the CAD literature. We address the challenge - to explain AdaBoost classification - with a novel algorithm that extracts simple, logical rules from AdaBoost models. Our algorithm, Adaptive-Weighted High Importance Path Snippets (Ada-WHIPS), makes use of AdaBoost's adaptive classifier weights. Using a novel formulation, Ada-WHIPS uniquely redistributes the weights among individual decision nodes of the internal decision trees of the AdaBoost model. Then, a simple heuristic search of the weighted nodes finds a single rule that dominated the model's decision. We compare the explanations generated by our novel approach with the state of the art in an experimental study. We evaluate the derived explanations with simple statistical tests of well-known quality measures, precision and coverage, and a novel measure stability that is better suited to the XAI setting.

RESULTS : Experiments on 9 CAD-related data sets showed that Ada-WHIPS explanations consistently generalise better (mean coverage 15%-68%) than the state of the art while remaining competitive for specificity (mean precision 80%-99%). A very small trade-off in specificity is shown to guard against over-fitting which is a known problem in the state of the art methods.

CONCLUSIONS : The experimental results demonstrate the benefits of using our novel algorithm for explaining CAD AdaBoost classifiers widely found in the literature. Our tightly coupled, AdaBoost-specific approach outperforms model-agnostic explanation methods and should be considered by practitioners looking for an XAI solution for this class of models.

Hatwell Julian, Gaber Mohamed Medhat, Atif Azad R Muhammad

2020-Oct-02

AdaBoost, Black box problem, Computer aided diagnostics, Explainable AI, Interpretability