The diagnosis of tuberculous meningitis: advancements in new technologies and machine learning algorithms

Shi, Yi; Zhang, Chengxi; Pan, Shuo; Chen, Yi; Miao, Xingguo; He, Guoqiang; Wu, Yanchan; Ye, Hui; Weng, Chujun; Zhang, Huanhuan; Zhou, Wenya; Yang, Xiaojie; Liang, Chenglong; Chen, Dong; Hong, Liang; Su, Feifei

doi:10.3389/fmicb.2023.1290746

Cited by 5 publications

(3 citation statements)

References 84 publications

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“…Integrating Grad-CAM with deep learning improved the efficiency and accuracy of disease detection, providing valuable information for medical professionals. Shi et al [25] employed ML techniques to diagnose tuberculous Meningitis, offering a potential solution to enhance diagnostic accuracy.…”

Section: Models Explainibilitymentioning

confidence: 99%

Towards XAI agnostic explainability to assess differential diagnosis for Meningitis diseases

Messai,

Drif,

Ouyahia

et al. 2024

Mach. Learn.: Sci. Technol.

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Meningitis, characterized by meninges and cerebrospinal fluid (CSF) inflammation, poses diagnostic challenges due to diverse clinical manifestations. This work introduces an explainable AI automatic medical decision methodology that determines critical features and their relevant values for the differential diagnosis of various meningitis cases. We proceed with knowledge acquisition to define the rules for this research. Currently, we have established the etiological diagnosis of Meningococcaemia, Meningococcal Meningitis, Tuberculous Meningitis, Aseptic Meningitis, Haemophilus influenzae Meningitis, and PneumococcalMeningitis. The data preprocessing was conducted after collecting data from samples with meningitis diseases at Setif Hospital in Algeria. Tree-based ensemble methods were then applied to assess the model’s performance. Finally, we implement an XAI agnostic explainability approach based on the SHapley Additive exPlanations technique to attribute each feature’s contribution to the model’s output. Experiments were conducted on the collected dataset and the SINAN database, obtained from the Brazilian Government’s Health Information System on Notifiable Diseases, which comprises 6729 patients aged over 18 years. The Extreme Gradient Boosting model was chosen for its superior performance metrics (Accuracy: 0.90, AUROC: 0.94, and F1-score: 0.98). Setif’s hospital data revealed notable performance metrics (Accuracy: 0.7143, F1-Score: 0.7857). This study's findings showcase each feature's contribution to the model’s predictions and diagnosis. It also reveals critical biomarker ranges associated with distinct types of Meningitis. Significant diagnostic effect was found for Meningococcal Meningitis with elevated neutrophil levels (>40%) and balanced lymphocyte levels (40-60%). Tuberculous Meningitis demonstrated low neutrophil levels (<60%) and elevated lymphocyte levels (>60%). Haemophilus influenzae meningitis exhibited a predominance of neutrophils (>80%), while Aseptic meningitis showed lower neutrophil levels (<40%) and lymphocyte levels within the range of 50-60%. The majority of the AI automatic medical decision results are twinned with validation by our team of infectious disease experts, confirming the alignment of algorithmic diagnoses with clinical practices.

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Section: Models Explainibilitymentioning

confidence: 99%

Towards XAI agnostic explainability to assess differential diagnosis for Meningitis diseases

Messai,

Drif,

Ouyahia

et al. 2024

Mach. Learn.: Sci. Technol.

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“…In addition, ML is excellent in data mining, especially for handling complex relationships towards prediction. This characteristic makes ML suitable for omics-based large-scale, high-dimensional datasets 34 , 35 . ML has also gained attention for improving the accuracy of clinical diagnosis 35 , 36 .…”

Section: Introductionmentioning

confidence: 99%

“…This characteristic makes ML suitable for omics-based large-scale, high-dimensional datasets 34 , 35 . ML has also gained attention for improving the accuracy of clinical diagnosis 35 , 36 . Indeed, ML has been used to accurately distinguish between NTM pulmonary disease and pulmonary TB (using CT image data) 37 .…”

Section: Introductionmentioning

confidence: 99%

Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning

Anh,

Phat,

Thu

et al. 2024

Sci Rep

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Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehensive statistical modeling to discover potential biomarkers for the differential diagnosis of NTM infection versus TB. Urine samples from 19 NTM and 35 TB patients were collected, and untargeted metabolomics was performed using rapid liquid chromatography-mass spectrometry. The urine metabolome was analyzed using a combination of univariate and multivariate statistical approaches, incorporating machine learning. Univariate analysis revealed significant alterations in amino acids, especially tryptophan metabolism, in NTM infection compared to TB. Specifically, NTM infection was associated with upregulated levels of methionine but downregulated levels of glutarate, valine, 3-hydroxyanthranilate, and tryptophan. Five machine learning models were used to classify NTM and TB. Notably, the random forest model demonstrated excellent performance [area under the receiver operating characteristic (ROC) curve greater than 0.8] in distinguishing NTM from TB. Six potential biomarkers for NTM infection diagnosis, including methionine, valine, glutarate, 3-hydroxyanthranilate, corticosterone, and indole-3-carboxyaldehyde, were revealed from univariate ROC analysis and machine learning models. Altogether, our study suggested new noninvasive biomarkers and laid a foundation for applying machine learning to NTM differential diagnosis.

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Biomarker Detection and Validation for Corneal Involvement in Patients With Acute Infectious Conjunctivitis

Seitzman,

Prajna,

Prajna

et al. 2024

JAMA Ophthalmol

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ImportanceInfectious conjunctivitis can lead to corneal involvement and result in ocular morbidity. The identification of biomarkers associated with corneal involvement has the potential to improve patient care.ObjectiveTo identify biomarkers in patients with acute infectious conjunctivitis.Design, Setting, and ParticipantsThis cross-sectional study took place from December 2016 to March 2024. Analyses were performed in 3 phases. First, logistic regression and machine learning algorithms were used to predict the probability of demonstrating corneal involvement in patients with presumed infectious conjunctivitis. Second, quantitative reverse transcription polymerase chain reaction (RT-qPCR) was used to confirm the most important biomarker gene identified by the algorithm. Third, the biomarker gene was validated in prospectively collected conjunctival samples of adult patients from 3 outpatient centers in Thailand and 1 in India. Patients with signs and symptoms of infectious conjunctivitis and onset within less than 14 days were eligible. Exclusion criteria were the inability to consent, presumed toxicity, or allergic conjunctivitis.ExposuresAcute infectious conjunctivitis.Main Outcomes and MeasuresThe identification and validation of ocular surface gene expression associated with corneal findings on slitlamp examination.ResultsThirteen genes exhibited a 1.5–log2 fold change in expression in patients with corneal involvement compared to patients without corneal involvement. Using the 13 genes to train and cross validate, logistic regression produced the highest mean area under the receiver operating characteristic curve (AUROC; 0.85; 95% CI, 0.84-0.86) for corneal involvement. The removal of apolipoprotein E (APOE) from the gene ensemble led to a decline in predictive performance of the logistic regression classifier (from mean AUROC 0.85 [95% CI, 0.84-0.86] to 0.74 [95% CI, 0.73-0.75]; adjusted P = .001 [Tukey test]). Orthogonal testing of APOE expression level with RT-qPCR showed that APOE expression was higher in patients with corneal involvement compared to patients without (median [IQR], 0.23 [0.04-0.47] vs 0.04 [0.02-0.06]; P = .004 [Mann-Whitney U test]). Using a Youden index of 0.23 Δ threshold cycle, APOE had a sensitivity of 56% (95% CI, 33-77) and a specificity of 88% (95% CI, 79-93) in 106 samples with conjunctivitis at Aravind, India (P &lt; .001 [Fisher exact test]). When applied to a different patient population in Thailand, the same criteria could discriminate between disease states (58 samples; sensitivity, 47%; 95% CI, 30-64 and specificity, 93%; 95% CI, 77-99; P = .001 [Fisher exact test]).Conclusions and RelevanceThe results from this study suggest that the host conjunctival immune response can be meaningfully interrogated to identify biomarkers for ocular surface diseases.

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The diagnosis of tuberculous meningitis: advancements in new technologies and machine learning algorithms

Cited by 5 publications

References 84 publications

Towards XAI agnostic explainability to assess differential diagnosis for Meningitis diseases

Towards XAI agnostic explainability to assess differential diagnosis for Meningitis diseases

Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning

Biomarker Detection and Validation for Corneal Involvement in Patients With Acute Infectious Conjunctivitis

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