AI-Powered Revolution: Convolutional Neural Networks Outperform Traditional Methods in the Rapid Detection of Antibiotic Resistance

Abstract

Background: The problem of antibiotic resistance is becoming a threat to health on the planet, especially where health infrastructure is lacking. Bacteria like Escherichia coli, Klebsiella pneumoniae and Staphylococcus aureus have those properties that make them multidrug resistant, hence eluding treatment protocols because they gain resistance to drugs. Although being efficient, the traditional diagnostic techniques are laborious and laboratory conditional.

Aim of study:

The study was carried out to examine the diagnostic accuracy of artificial intelligence (AI) in identifying bacterial antibiotic resistance on a convolutional neural network (CNN) and also to compare the accuracy as well as efficiency of the methods with that of conventional methods.

Methods:

The systematic review procedure was performed with the use of such databases as PubMed as well as Scopus and Google Scholar. Publications published in 2019 to 2024 were selected using inclusion criteria on comparative performance indicators of sensitivity, specificity, PPV, NPV and the diagnostic timings. SPSS v27 was used to carry out meta-analysis. Results:

AI models proved to be better in diagnosis with sensitivity of 92.95% and specificity 88.92% than 75.85% and 70.80% respectively of conventional methods. The time consumed in the diagnosis came down to 24-72hours to the lowest 30 minutes in certain AI applications. Also, AI enhanced the accuracy of antibiotics selection greatly, making all inappropriate prescriptions drop by 85%, resulting in 1%. Conclusions: it can be stated that the AI-based diagnostics can be used as a rather promising alternative to the conventional ones as the resistance patterns are revealed in a quicker and more precise way. Their integration into clinical practice and operation may optimize the therapeutic choice and lower the incidences of an unfortunate outcome in numerous resource-poor countries such as Iraq. There should be Drug-Resistant.