Critical Evaluation of Scholarly Articles – A Synopsis of Key Findings

Critical Evaluation of Scholarly Articles – A Synopsis of Key Findings

An article by Bertocchi et al., 2019, titled “Classification of mood disorders using a multiple kernel approach on multimodal neuroimaging data.” discusses the high number of misdiagnoses in patients with bipolar disorder (BD) who are every so often initially diagnosed with major depressive disorder (MDD). According to the article, after the initial misdiagnosis, a patient lasts about 5 to 10 years with that diagnosis before they are correctly diagnosed, whose consequences include adverse clinical outcomes, increased suicide rates, and higher healthcare costs. In addition, the article argues that traditional neuroimaging methods, which focus on group-level differences, are insufficient for individual diagnosis. As such, the development of new artificial intelligence (AI) and machine learning (ML) procedures was the solution in order to improve individual predictions based on neuroimaging data. The study’s main objective is to apply ML to multimodal structural neuroimaging data, specifically focusing on white matter microstructure integrity and grey matter volume, in order to tell apart the two disorders, particularly in depressed patients. In this procedure, the researchers used tract-based spatial statistics (TBSS) on diffusion tensor imaging (DTI) measures of white matter microstructure and brain imaging volumetric T1-weighted sequences. Subsequently, the data obtained was then processed and evaluated using several specialized software tools. Notably, multiple kernel learning (MKL) was employed to train the model and enhance its parameters.

Based on the outcomes, MKL proved that it could distinguish between MDD and BD with an accuracy of 69.3%, a sensitivity of 77.14% for BD, and a specificity of 61.4% for MDD. The classifier’s positive predictive values were 72.88% for BD and 66.7% for MDD, with an area under the ROC curve (AUC) of 0.73. The conclusion of this research was that MKL can effectively differentiate between BD and MDD on the basis of structural neuroimaging profiles. This makes it a promising device for early differential diagnosis as well as possibly improving prevention and treatment strategies for disorders. It recommends that neuroimaging biomarkers could be employed in clinical practice and ought to be validated in longitudinal research.

The second article is by Wang, T., Xue, C., Zhang, Z., Cheng, T., & Yang, G. (2024), titled “Unraveling the distinction between depression and anxiety: A machine learning exploration of causal relationships.” In this article, the researchers contend that although depression and anxiety have some common clinical traits, they are different disorders with dissimilar management strategies. The typical criteria used to distinguish between them, including the International Classification of Diseases-10 (ICD-10) and the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), can lead to misdiagnosis. As such, they state that there is a need for devices that precisely differentiate between depression and anxiety to improve treatment outcomes. Accordingly, machine learning algorithms, developed since the mid-20th century, have improved medical diagnoses, including depression. These tools can distinguish between subclinical and clinical levels of anxiety and depression. The Symptom Checklist 90 is an extensively used questionnaire to evaluate psychological problems. This study used causal inference to make the SCL-90, extracting elements associated with depression, anxiety, or both. These simplified items were subsequently applied in machine learning algorithms to create efficient, cost-effective classification models to differentiate between depression and anxiety.

Based on my understanding of the two articles, misdiagnosis in both anxiety and mood disorders has been a common occurrence in both fields, which has led to devastating results. However, based on these two articles, machine learning (ML) proves to be an effective technique in diagnosing mood and anxiety disorders, offering the possibility of more accurate, timely, and personalized mental health care. In addition, Parker et al. (2022) highlight the severity, impairment, and suicide risks related to mood disorders and how accurate diagnosis is crucial for mitigating these effects. In a rapidly growing technology world, such machine learning techniques are extremely important and as technology evolves, the problems that people have faced with anxiety and mood disorders will be eliminated.

References

Bertocchi, C., Vai, B., Poletti, S., Bollettini, I., Melloni, E., Colombo, C., & Benedetti, F. (2019). P. 4.12 Classification of mood disorders using a multiple kernel approach on multimodal neuroimaging data. European Neuropsychopharmacology, 29, S709-S710.

Parker, G., Spoelma, M. J., Tavella, G., Alda, M., Dunner, D. L., O’Donovan, C., … & Manicavasagar, V. (2022). A new machine learning-derived screening measure for differentiating bipolar from unipolar mood disorders. Journal of Affective Disorders, 299, 513-516.

Wang, T., Xue, C., Zhang, Z., Cheng, T., & Yang, G. (2024). Unraveling the distinction between depression and anxiety: A machine learning exploration of causal relationships. Computers in Biology and Medicine, 174, 108446.

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Supporting Lectures:
Defining Anxiety Disorders
Defining Mood Disorders
In this assignment, you will review current scholarly journal articles and provide a critical evaluation of the studies by creating a synopsis of your findings.

Critical Evaluation of Scholarly Articles – A Synopsis of Key Findings

Tasks:
Find and analyze two scholarly journal articles from the South University Online Library: one describing the classification of mood disorders and one describing the classification of anxiety disorders. Scholarly journal articles are also referred to as primary sources or peer-reviewed articles. Each article must have been published within the past five years. Other sources such as the text (other than supporting citations), Wikipedia, and other online sources will not be accepted.