Progress and trends in neurological disorders research based on deep learning.

In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging, offering unprecedented opportunities for the diagnosis and treatment of neurological disorders (NDs). This comprehensive review explores the multifaceted role of DL techniques in leveraging vast datasets to advance our understanding of NDs and improve clinical outcomes. Beginning with a systematic literature review, we delve into the utilization of DL, particularly focusing on multimodal neuroimaging data analysis-a domain that has witnessed rapid progress and garnered significant scientific interest. Our study categorizes and critically analyses numerous DL models, including Convolutional Neural Networks (CNNs), LSTM-CNN, GAN, and VGG, to understand their performance across different types of Neurology Diseases. Through particular analysis, we identify key benchmarks and datasets utilized in training and testing DL models, shedding light on the challenges and opportunities in clinical neuroimaging research. Moreover, we discuss the effectiveness of DL in real-world clinical scenarios, emphasizing its potential to revolutionize ND diagnosis and therapy. By synthesizing existing literature and describing future directions, this review not only provides insights into the current state of DL applications in ND analysis but also covers the way for the development of more efficient and accessible DL techniques. Finally, our findings underscore the transformative impact of DL in reshaping the landscape of clinical neuroimaging, offering hope for enhanced patient care and groundbreaking discoveries in the field of neurology. This review paper is beneficial for neuropathologists and new researchers in this field.

A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images.

INTRODUCTION: Pneumonia is a microorganism infection that causes chronic inflammation of the human lung cells. Chest X-ray imaging is the most well-known screening approach used for detecting pneumonia in the early stages. While chest-Xray images are mostly blurry with low illumination, a strong feature extraction approach is required for promising identification performance. OBJECTIVES: A new hybrid explainable deep learning framework is proposed for accurate pneumonia disease identification using chest X-ray images. METHODS: The proposed hybrid workflow is developed by fusing the capabilities of both ensemble convolutional networks and the Transformer Encoder mechanism. The ensemble learning backbone is used to extract strong features from the raw input X-ray images in two different scenarios: ensemble A (i.e., DenseNet201, VGG16, and GoogleNet) and ensemble B (i.e., DenseNet201, InceptionResNetV2, and Xception). Whereas, the Transformer Encoder is built based on the self-attention mechanism with multilayer perceptron (MLP) for accurate disease identification. The visual explainable saliency maps are derived to emphasize the crucial predicted regions on the input X-ray images. The end-to-end training process of the proposed deep learning models over all scenarios is performed for binary and multi-class classification scenarios. RESULTS: The proposed hybrid deep learning model recorded 99.21% classification performance in terms of overall accuracy and F1-score for the binary classification task, while it achieved 98.19% accuracy and 97.29% F1-score for multi-classification task. For the ensemble binary identification scenario, ensemble A recorded 97.22% accuracy and 97.14% F1-score, while ensemble B achieved 96.44% for both accuracy and F1-score. For the ensemble multiclass identification scenario, ensemble A recorded 97.2% accuracy and 95.8% F1-score, while ensemble B recorded 96.4% accuracy and 94.9% F1-score. CONCLUSION: The proposed hybrid deep learning framework could provide promising and encouraging explainable identification performance comparing with the individual, ensemble models, or even the latest AI models in the literature. The code is available here: https://github.com/chiagoziemchima/Pneumonia_Identificaton.

Efficacy of Acacia nilotica Linn. Pod's Sitz Bath plus Vaginal Pessary in Syndromic Management of Abnormal Vaginal Discharge: A Randomized Controlled Trial.

Objectives: Abnormal vaginal discharge (Sayalan al-Rahim) is a common public health problem that significantly disrupts the health-related quality of life (HRQoL). Syndromic management infers the concurrent treatment of two or more infections. Hence, a comparative, single-blind study was planned to determine the efficacy of Acacia (Acacia nilotica Linn.) pod's sitz bath (Abzan) plus vaginal pessary (Farzaja) vs. placebo in abnormal vaginal discharge syndromic management, its associated symptoms, and women's HRQoL. Methods: Diagnosed patients (n = 66) were randomly divided into Acacia (n = 33) and placebo (n = 33) group. Acacia group received Sitz bath with Acacia pod powder (30g) solution followed by vaginal cotton pessary (5 ml of the same solution) once daily for 10 days. The placebo group received palm sugar powder (30g) solution for Sitz bath plus vaginal cotton pessary same as the Acacia group. Primary outcomes included clinical cure assessed with VAS for symptoms and Modified McCormack Pain Scale (McPS) for pelvic tenderness. The secondary outcomes included were the EQ-5D-5 L questionnaire, TSQM questionnaire, sachet count, and microbiological cure. Overall, therapeutic cure included clinical and microbiological cure after treatment. Results: The overall therapeutic cure for bacterial vaginosis, cervicitis, and uncomplicated pelvic inflammatory disease was 100% (n = 7/7), 45.45% (n = 10/22), and 71.42% (n = 5/7), respectively, in the Acacia group, while in the placebo group none of the patients had responded. The VAS score for symptoms was significantly reduced in Acacia than in the placebo group. At each follow-up, the improvement in the EQ-5D-5 L level of HRQoL was significantly higher in the Acacia group than in the placebo group. Conclusion: Acacia would be an effective and safe alternative in syndromic management of abnormal vaginal discharge, associated symptoms, and improved women's HRQoL. Trial registration. This trial was registered in the Clinical Trials Registry of Indian Trials Website and given the identification no. CTRI/2018/02/012175 (dated: 27/02/2018).