Dataset Information

Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning.

ABSTRACT: The problem of automating the data analysis of microplastics following a spectroscopic measurement such as focal plane array (FPA)-based micro-Fourier transform infrared (FTIR), Raman, or QCL is gaining ever more attention. Ease of use of the analysis software, reduction of expert time, analysis speed, and accuracy of the result are key for making the overall process scalable and thus allowing nonresearch laboratories to offer microplastics analysis as a service. Over the recent years, the prevailing approach has been to use spectral library search to automatically identify spectra of the sample. Recent studies, however, showed that this approach is rather limited in certain contexts, which led to developments for making library searches more robust but on the other hand also paved the way for introducing more advanced machine learning approaches. This study describes a model-based machine learning approach based on random decision forests for the analysis of large FPA-μFTIR data sets of environmental samples. The model can distinguish between more than 20 different polymer types and is applicable to complex matrices. The performance of the model under these demanding circumstances is shown based on eight different data sets. Further, a Monte Carlo cross validation has been performed to compute error rates such as sensitivity, specificity, and precision.

SUBMITTER: Hufnagl B

PROVIDER: S-EPMC8757466 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Laser interstitial thermal therapy (LITT) is an alternative to open surgery for drug-resistant focal mesial temporal lobe epilepsy (MTLE). Studies suggest maximal ablation of the mesial hippocampal head and amygdalohippocampal complex (AHC) improves seizure freedom rates while better neuropsychological outcomes are associated with sparing of the parahippocampal gyrus (PHG). Optimal trajectories avoid sulci and CSF cavities and maximize distance from vasculature. Computer-assisted planning (CAP) improves these metrics, but the combination of entry and target zones has yet to be determined to maximize ablation of the AHC while sparing the PHG. We apply a machine learning approach to predict entry and target parameters and utilize these for CAP. Ten patients with hippocampal sclerosis were identified from a prospectively managed database. CAP LITT trajectories were generated using entry regions that include the inferior occipital, middle occipital, inferior temporal, and middle temporal gyri. Target points were varied by sequential AHC erosions and transformations of the centroid of the amygdala. A total of 7600 trajectories were generated, and ablation volumes of the AHC and PHG were calculated. Two machine learning approaches (random forest and linear regression) were investigated to predict composite ablation scores and determine entry and target point combinations that maximize ablation of the AHC while sparing the PHG. Random forest and linear regression predictions had a high correlation with the calculated values in the test set (ρ = 0.7) for both methods. Maximal composite ablation scores were associated with entry points around the junction of the inferior occipital, middle occipital, and middle temporal gyri. The optimal target point was the anteromesial amygdala. These parameters were then used with CAP to generate clinically feasible trajectories that optimize safety metrics. Machine learning techniques accurately predict composite ablation score. Prospective studies are required to determine if this improves seizure-free outcome while reducing neuropsychological morbidity following LITT for MTLE.

Dataset Information

Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning.

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