Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review

The application of artificial intelligence (AI) in diagnostic imaging has gained significant interest in recent years, particularly in lung cancer detection. This systematic review aims to assess the accuracy of machine learning (ML) AI algorithms in lung cancer detection, identify the ML architectures currently in use, and evaluate the clinical relevance of these diagnostic imaging methods. A systematic search of PubMed, Web of Science, Cochrane, and Scopus databases was conducted in February 2023, encompassing the literature published up until December 2022. The review included nine studies, comprising five case-control studies, three retrospective cohort studies, and one prospective cohort study. Various ML architectures were analyzed, including artificial neural network (ANN), entropy degradation method (EDM), probabilistic neural network (PNN), support vector machine (SVM), partially observable Markov decision process (POMDP), and random forest neural network (RFNN). The ML architectures demonstrated promising results in detecting and classifying lung cancer across different lesion types. The sensitivity of the ML algorithms ranged from 0.81 to 0.99, while the specificity varied from 0.46 to 1.00. The accuracy of the ML algorithms ranged from 77.8% to 100%. The AI architectures were successful in differentiating between malignant and benign lesions and detecting small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). This systematic review highlights the potential of ML AI architectures in the detection and classification of lung cancer, with varying levels of diagnostic accuracy. Further studies are needed to optimize and validate these AI algorithms, as well as to determine their clinical relevance and applicability in routine practice.

Comparison of Type I and Type III Collagen Concentration between Oreochromis mossambicus and Oreochromis niloticus in Relation to Skin Scaffolding

Background and Objectives: Skin scaffolding can be done using allografts and autografts. As a biological allograft, the skin of Oreochromis niloticus (ON) has been used due to its high type I and III collagen content. Oreochromis mossambicus (OM) is also a member of the Oreochromis family, but not much is known regarding its collagen content. As such, this study aimed to assess and compare the collagen content of the two fish species. Materials and Methods: This is a crossover study comparing the skin collagen contents of the two fish. Young fish were chosen, as they tend to have higher collagen concentrations. The skin samples were sterilized in chlorhexidine and increasing glycerol solutions and analyzed histochemically with Sirius red picrate under polarized light microscopy. Results: 6 young ON and 4 OM specimens were used. Baseline type I collagen was higher for OM, but at maximum sterilization it was higher for ON, with no differences in between Type III collagen was higher for OM across all comparisons with the exception of the last stage of sterilization. Generally, collagen concentrations were higher in highly sterilized samples. Conclusions: OM skin harvested from young fish, with its greater collagen III content may be a better candidate for use as a biological skin scaffold in the treatment of burn wounds, compared to ON.