An Alternative Diagnostic Method for C. neoformans: Preliminary Results of Deep-Learning Based Detection Model

A Machine Learning Model for Diagnosing Opportunistic Infections in HIV Patients: Broad Applicability Across Infection Types

Opportunistic infections (OIs) are the leading cause of hospitalisation and mortality among Human Immunodeficiency Virus-infected (HIV-infected) patients. The diverse pathogen types and intricate clinical manifestations associated present a formidable challenge to the timely diagnosis of these infections. This study aims to use machine learning techniques to develop a diagnostic model that quickly identifies whether HIV-infected patients have any type of OIs, without being limited to specific infections, thus adapting to various clinical scenarios. This study is a retrospective cohort study that collected clinical data from HIV-infected patients at four healthcare organisations in China. A total of twelve machine learning classification algorithms were employed for the purposes of model training and evaluation. Additionally, feature reduction was conducted through the implementation of an importance ranking, with the objective of eliminating any redundant features. In conclusion, both the five features based on Shapley additive explanations (procalcitonin, haemoglobin, lymphocyte, creatinine, platelet) and the five features based on Permutation Importance explanations (procalcitonin, lymphocyte, haemoglobin, creatinine, indirect bilirubin) achieved the highest F1 score when evaluated using the adaptive boosting classifier model. The scores on the test set were 0.9016 and 0.9063, respectively, which significantly outperformed the best 32-feature model, gradient boosting classifier, which had a test set F1 score of 0.8991.

The clinical prediction model to distinguish between colonization and infection by Klebsiella pneumoniae

Objective

To develop a machine learning-based prediction model to assist clinicians in accurately determining whether the detection of Klebsiella pneumoniae (KP) in sputum samples indicates an infection, facilitating timely diagnosis and treatment.

Research methods

A retrospective analysis was conducted on 8,318 patients with KP cultures admitted to a tertiary hospital in Northeast China from January 2019 to December 2023. After excluding duplicates, other specimen types, cases with substandard specimen quality, and mixed infections, 286 cases with sputum cultures yielding only KP were included, comprising 67 cases in the colonization group and 219 cases in the infection group. Antimicrobial susceptibility testing was performed on the included strains, and through univariate logistic regression analysis, 15 key influencing factors were identified, including: age > 62 years, ESBL, CRKP, number of positive sputum cultures for KP, history of tracheostomy, use of mechanical ventilation for >96 h, indwelling gastric tube, history of craniotomy, recent local glucocorticoid application, altered consciousness, bedridden state, diagnosed with respiratory infectious disease upon admission, electrolyte disorder, hypoalbuminemia, and admission to ICU (all p < 0.05). These factors were used to construct the model, which was evaluated using accuracy, precision, recall, F1 score, AUC value, and Brier score.

Results

Antimicrobial susceptibility testing indicated that the resistance rates for penicillins, cephalosporins, carbapenems, and quinolones were significantly higher in the infection group compared to the colonization group (all p < 0.05). Six predictive models were constructed using 15 key influencing factors, including Classification and Regression Trees (CART), C5.0, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), Random Forest (RF), and Nomogram. The Random Forest model performed best among all indicators (accuracy 0.93, precision 0.98, Brier Score 0.06, recall 0.72, F1 Score 0.83, AUC 0.99). The importance of each factor was demonstrated using mean decrease in Gini. "Admitted with a diagnosis of respiratory infectious disease" (8.39) was identified as the most important factor in the model, followed by "Hypoalbuminemia" (7.83), then "ESBL" (7.06), "Electrolyte Imbalance" (5.81), "Age > 62 years" (5.24), "The number of Positive Sputum Cultures for KP > 2" (4.77), and being bedridden (4.24). Additionally, invasive procedures (such as history of tracheostomy, use of ventilators for >96 h, and craniotomy) were also significant predictive factors. The Nomogram indicated that CRKP, presence of a nasogastric tube, admission to the ICU, and history of tracheostomy were important factors in determining KP colonization.

Conclusion

The Random Forest model effectively distinguishes between infection and colonization status of KP, while the Nomogram visually presents the predictive value of various factors, providing clinicians with a reference for formulating treatment plans. In the future, the accuracy of infection diagnosis can be further enhanced through artificial intelligence technology to optimize treatment strategies, thereby improving patient prognosis and reducing healthcare burdens.

Cryptococcosis with pulmonary cavitation in an immunocompetent child: a case report and literature review

BACKGROUND

Pulmonary cryptococcosis (PC) rarely occurs in immunocompetent children.

CASE PRESENTATION

A 13-year-old boy was admitted to the First Affiliated Hospital of Ningbo University in February 2023 with complaints of cough and chest pain. Physical examination showed slightly moist rales in the right lung. Chest computed tomography (CT) suggested a lung lesion and cavitation. Blood routine test, lymphocyte subsets, immunoglobulin, and complement tests indicated that the immune system was normal. However, the serum cryptococcal antigen test was positive. Next-generation sequencing revealed Cryptococcus infection. The child was diagnosed with PC and was discharged after treating with fluconazole 400 mg. Four months later, chest CT showed that the lung lesion diminished, and reexamination of serum cryptococcal antigen test turned positive.

CONCLUSION

PC should be considered in an immunocompetent child with pulmonary cavities with nonspecific symptoms.

Assessment of Deep Learning Models for Cutaneous Leishmania Parasite Diagnosis Using Microscopic Images

Cutaneous leishmaniasis (CL) is a common illness that causes skin lesions, principally ulcerations, on exposed regions of the body. Although neglected tropical diseases (NTDs) are typically found in tropical areas, they have recently become more common along Africa's northern coast, particularly in Libya. The devastation of healthcare infrastructure during the 2011 war and the following conflicts, as well as governmental apathy, may be causal factors associated with this catastrophic event. The main objective of this study is to evaluate alternative diagnostic strategies for recognizing amastigotes of cutaneous leishmaniasis parasites at various stages using Convolutional Neural Networks (CNNs). The research is additionally aimed at testing different classification models employing a dataset of ultra-thin skin smear images of Leishmania parasite-infected people with cutaneous leishmaniasis. The pre-trained deep learning models including EfficientNetB0, DenseNet201, ResNet101, MobileNetv2, and Xception are used for the cutaneous leishmania parasite diagnosis task. To assess the models' effectiveness, we employed a five-fold cross-validation approach to guarantee the consistency of the models' outputs when applied to different portions of the full dataset. Following a thorough assessment and contrast of the various models, DenseNet-201 proved to be the most suitable choice. It attained a mean accuracy of 0.9914 along with outstanding results for sensitivity, specificity, positive predictive value, negative predictive value, F1-score, Matthew's correlation coefficient, and Cohen's Kappa coefficient. The DenseNet-201 model surpassed the other models based on a comprehensive evaluation of these key classification performance metrics.