+ HEALTH Aims Intramuscular fat (IMF) is increasingly recognized as a marker of ectopic adiposity and adverse cardiome…+ HEALTH Periapical lesions are challenging to detect on intraoral radiographs because of anatomical superimposition a…± HEALTH Ultrasound imaging is an indispensable diagnostic tool, yet its profound reliance on operator expertise inher…+ HEALTH Objectives We adapted the individualized polysocial risk score (iPsRS), a machine learning model originally d…± HEALTH Melioidosis is a life-threatening infectious disease caused by Burkholderia pseudomallei ( Bp ). Rapid diagno…+ HEALTH Classification is a core machine learning task, and ensemble voting methods are widely used to improve predic…+ CLIMATE Microplastic (MP) pollution poses escalating environmental risks, demanding efficient and reproducible tools…± HEALTH Medicinal chemistry sits at the center of modern drug discovery, yet translating molecular designs into appro…
TruaceTracing the truth around AIWednesday, August 26, 2026
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Climate·G Space·Evidence-backed gain·Published 2026-08-26

Developing and evaluating automated deep learning and human-in-the-loop vision-language systems for microplastic characterization

Abstract: Microplastic (MP) pollution poses escalating environmental risks, demanding efficient and reproducible tools for morphological characterization of plastic particles. Traditional manual microscopy is labour-intensive, operator-dependent, and poorly suited to large-scale monitoring. This study presents a comparative evaluation of two distinct artificial intelligence paradigms for the analysis of optical microscope images of microplastics. The first paradigm is a domain-specific, multi-task deep learning (DL) class…

TRV-2026-0894Peer-reviewedPermanent record — cite & verify
Developing and evaluating automated deep learning and human-in-the-loop vision-language systems for microplastic characterization

Statistical profiles of particles’ size and shape for each plastic polymer found in plastic-bottled water by Authors of the study: Naixin Qian https://orcid.org/0000-0001-6433-063X , Xin Gao https://orcid.org/0000-0002-0911-3656 , Xiaoqi Lang, Huiping Deng, Teodora Maria Bratu, Qixuan Chen, Phoebe Stapleton https://orcid.org/0000-0003-3049-4868 , Beizhan Yan https://orcid.org/0000-0002-1321-779X yanbz@ldeo.columbia.edu, and Wei Min. CC BY 4.0 · https://creativecommons.org/licenses/by/4.0

The quick read

Researchers compared a domain-specific EfficientNet-B0 multi-task deep learning classifier trained on about 700 annotated optical microscope images against a zero-shot Claude Vision API augmented with expert human-in-the-loop guidance. Both were tested on the same independent test set for predicting microplastic shape/type, color, and surface texture, with the DL model reaching F1-scores of 91.2%, 88.5% and 85.1% and the VLM improving from 72-81% to 84-89% after refinement.

Automated morphological characterization could reduce labor and operator dependence in microplastic pollution monitoring, enabling more reproducible large-scale environmental assessment. As of the August 2026 publication date, results are limited to a small in-house image set and a single test set, leaving open how well either approach generalizes to diverse field samples, imaging conditions, and broader monitoring workflows.

Main points
  • Study compared two AI paradigms on identical independent test set using accuracy, macro-averaged precision, recall, and F1-score.
  • Domain-specific multi-task deep learning classifier based on EfficientNet-B0 with transfer learning trained on approximately 700 annotated images to predict five shape/type classes, 10 color classes, and two texture classes.
  • Zero-shot Claude Vision API augmented with structured human-in-the-loop mechanism allowing domain experts to provide targeted guidance for ambiguous particles improved from 72-81% raw F1 to approximately 84-89% after refinement.
  • Comparative framework deployed as freely accessible web application via Hugging Face Spaces.
Gain

Domain-specific EfficientNet-B0 classifier and Claude Vision API with human-in-the-loop guidance automated morphological characterization of microplastics from optical microscope images, achieving high F1-scores for shape/type, color and texture.

The rundown

Traditional manual microscopy described as labour-intensive, operator-dependent, and poorly suited to large-scale monitoring, motivating automated alternatives. Study evaluated both systems on identical independent test set.

DL classifier excelled in high-throughput reproducible screening while VLM-HITL offered interpretability and flexibility for ambiguous cases. System made available via Hugging Face Spaces as practical deployment example.

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