Using Artificial Intelligence in Diagnostics of Meningitis

Šeho, L.; Šutković, H.; Tabak, V.; Tahirović, S.; Smajović, Alisa; Bečić, Ervina; Deumić, Amar; Bećirović, Lemana Spahić; Pokvić, Lejla Gurbeta; Badnjević, Almir

doi:10.1016/j.ifacol.2022.06.009

Cited by 13 publications

(17 citation statements)

References 40 publications

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“…The evidence from this review shows that in all studies, ML techniques have been an effective and positive approach to facilitating disease diagnosis and prediction. According to the findings of our study, in eight cases of incoming studies, ML algorithms were used to diagnose the disease, and all studies indicated the high ability of ML algorithms to diagnose meningitis diseases 48,49,52,54–56,60,61 . So even in Jeong and colleagues study, it was stated that the ANN model can have better diagnostic performance than a nonspecialist doctor.…”

Section: Discussionmentioning

confidence: 69%

“…The majority of the included studies (56%) were carried out in low‐ and middle‐income countries (LMICs), according to the results of our systematic review 46–48,50,56,57,60,61 . This can become a strong point for improving primary care services and thus improving their economies, especially due to the unequal distribution of resources and health care provision in LMICs.…”

Section: Discussionmentioning

confidence: 99%

“…The ML algorithms used in the studies include logistic regression (LR) (25%), 46,48,51,54 multiple logistic regression (MLR) (25%), 50,52,53,61 support vector machine (SVM) (19%), 48,54,56 artificial neural network (ANN) (19%), 47,54,60 random forest (RF) (19%), 52,54,56 decision tree (DT) (12%), 57,58 nave-Bayes (NB) (12%), 52,54 fast-and-frugal trees (FFTs) algorithm (6%), 56 and unsupervised ML approach (6%). 59 The target meningitis diseases in the studies included bacterial meningitis (BM) (37%) [51][52][53]56,57,61 or enteroviral meningitis (EVM) (6%), 56 tuberculous meningitis (TBM) (25%), 48,54,55,61 viral meningitis (VM) (19%), 52,54,55 Neisseria meningitides (6%), 59 lumbar drainagerelated meningitis (LDRM) (6%), 47 healthcare-associated ventriculitis and meningitis (HAVM) (6%), 58 cryptococcal meningitis (CM) (6%), 50 PM (6%), 46 and pediatric purulent meningitis (PPM) (6%).…”

Section: Study Characteristicsmentioning

confidence: 99%

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The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review

Ghaddaripouri,

Mousavi

et al. 2024

Health Science Reports

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Background and AimsThis systematic review aimed to evaluating the effectiveness of machine learning (ML) algorithms for the prediction and diagnosis of meningitis.MethodsOn November 12, 2022, a systematic review was carried out using a keyword search in the reliable scientific databases PubMed, EMBASE, Scopus, and Web of Science. The recommendations of Preferred Reporting for Systematic Reviews and Meta‐Analyses (PRISMA) were adhered to. Studies conducted in English that employed ML to predict and identify meningitis were deemed to match the inclusion criteria. The eligibility requirements were used to independently review the titles and abstracts. The whole text was then obtained and independently reviewed in accordance with the eligibility requirements.ResultsAfter all the research matched the inclusion criteria, a total of 16 studies were added to the systematic review. Studies on the application of ML algorithms in the three categories of disease diagnosis ability (8.16) and disease prediction ability (8.16) (including cases related to identifying patients (50%), risk of death in patients (25%), the consequences of the disease in childhood (12.5%), and its etiology [12.5%]) were placed. Among the ML algorithms used in this study, logistic regression (LR) (4.16, 25%) and multiple logistic regression (MLR) (4.16, 25%) were the most used. All the included studies indicated improvements in the processes of diagnosis, prediction, and disease outbreak with the help of ML algorithms.ConclusionThe results of the study showed that in all included studies, ML algorithms were an effective approach to facilitate diagnosis, predict consequences for risk classification, and improve resource utilization by predicting the volume of patients or services as well as discovering risk factors. The role of ML algorithms in improving disease diagnosis was more significant than disease prediction and prevalence. Meanwhile, the use of combined methods can optimize differential diagnoses and facilitate the decision‐making process for healthcare providers.

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Section: Discussionmentioning

confidence: 69%

Section: Discussionmentioning

confidence: 99%

Section: Study Characteristicsmentioning

confidence: 99%

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The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review

Ghaddaripouri,

Mousavi

et al. 2024

Health Science Reports

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“…Here, the AI-based methods are useful for avoiding invasive sampling. In [ 152 ], the authors proposed an ANN-based method to detect the presence of meningitis based on temperature and various blood parameters with an accuracy of 96.67%, and thus, the proposed approach is found to serve as an alternate to invasive procedures, which involve a lengthy diagnosis time and more importantly, the individual remains unresponsive during the diagnosis, and the invasive methods produce stress in individuals.…”

Section: Applications Of Ai For Neurological Disordersmentioning

confidence: 99%

Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review

Surianarayanan

Lawrence

Raj

et al. 2023

Sensors

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Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.

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“…Die richtige Therapie ist jedoch entscheidend für das Outcome. Anhand weniger Faktoren, u. a. Temperatur, Serum-/Liquor-Glukose-Index und Leukozyten im Liquor gelang es einer Forschungsgruppe aus Bosnien und Herzegowina, einen Algorithmus zu entwickeln, der mit einer Genauigkeit von knapp 97 % bakterielle und virale Meningitiden richtig differenzieren kann [33]. Eine rein auf das Blut reduzierte Faktorenanalyse konnte keine ausreichende Sensitivität und Spezifität erreichen [34].…”

Section: Neuroinfektiologieunclassified

Künstliche Intelligenz in der Neurologie

Ernst,

Rémi

2023

Nervenheilkunde

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ZUSAMMENFASSUNGKünstliche Intelligenz (KI) ist spätestens seit der Veröffentlichung von ChatGPT in aller Munde. Die Grundlage eines jeden KI-Modells ist die Analyse von Daten. In der Neurologie sind aufgrund der Digitalisierung ausreichend große Datenmengen vorhanden, um mittels KI analysiert werden zu können. Dieser Artikel soll einen Überblick über KI-Modelle sowie aktuelle Forschungen und Anwendungen in der Neurologie geben. Mögliche Probleme in der Integration der KI in den klinischen Alltag werden beleuchtet und ein Ausblick auf die Zukunft wird versucht.

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Using Artificial Intelligence in Diagnostics of Meningitis

Cited by 13 publications

References 40 publications

The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review

The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review

Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review

Künstliche Intelligenz in der Neurologie

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