The study, published in the Journal of Micropalaeontology, is, according to its authors, the first attempt to apply deep learning to the automated identification of Devonian miospores. The team, led by Vitalina Lokteva and corresponding author Vahram Serobyan of the Institute of Geological Sciences of the National Academy of Sciences of Armenia, together with Martin Tetard, Pierre Breuer, and Taniel Danelian, focused on three species of enormous biostratigraphic value: Teichertospora torquata, Geminospora lemurata, and Samarisporites triangulatus. These spores are widely used to define global biozones across the ancient continents of Gondwana and Laurussia, and they play a particularly important role in hydrocarbon exploration, notably in Saudi Arabia, where Devonian spore-based stratigraphy helps geologists date and correlate the rock layers that host oil and gas reservoirs.

The material came from the Ertych section in central Armenia, one of the region’s best-known localities for palynological research. Fourteen beds of upper Frasnian age were sampled during several field campaigns, and productive miospore assemblages were recovered from twelve of them. Standard palynological preparation yielded three to five slides per sample, which were examined with a Leica DM2700 transmitted-light microscope. Every specimen that a specialist qualitatively identified as one of the three target species was photographed individually with a Flexacam C5 camera, producing a library of 209 microphotographs that formed the raw material for the machine learning pipeline.

Each of those 209 images was then annotated by hand using the web-based platform Roboflow. An operator drew a rectangular bounding box around every individual spore and recorded the image identifier, class label, and microscope magnification as metadata. The resulting detection dataset contained five classes: the three target species, which together accounted for roughly 57 percent of the annotations, an “other” class comprising non-target miospore taxa, and a background class dominated by images without miospores, filled largely with fragments of plant debris known as phytoclasts. The images were divided into training, validation, and test subsets in a 70/20/10 split, with the test set kept strictly separate so that no image used in evaluation had ever been seen during training.

The architecture of the workflow reflects a growing consensus in automated microscopy: detection first, classification second. In the detection stage, the team deployed YOLOv11, a one-stage object detection network that predicts bounding-box coordinates, objectness scores, and class probabilities in a single forward pass. Initialized with weights pretrained on the COCO image dataset and trained with a single class label of “miospore”, the detector was applied to the test micrographs using a confidence threshold of 0.7. Its performance was striking. Precision, the fraction of predicted boxes that were correct, reached 0.98; recall, the fraction of true objects the model actually found, was a perfect 1.0; and the mean average precision at an intersection-over-union threshold of 0.5 came in at 0.995, averaging 0.906 across the stricter range of thresholds from 0.5 to 0.95.

Crucially, the detection stage did more than simply find the spores. For each detection, the pipeline stored the bounding-box coordinates, a cropped image patch stripped of its cluttered background, and the area of the crop, measured in pixels and calibrated to square micrometers. This inference step produced 159 background-free spore crops, which became the classification dataset, split 70/15/15 into training, validation, and test subsets. The bounding-box area was retained as an auxiliary numeric feature, a simple piece of morphometric information that would later prove surprisingly valuable when taxa differed mainly in size.

For the classification stage, the researchers compared three convolutional neural networks, all implemented in PyTorch and trained through transfer learning, in which the pretrained convolutional feature extractors were frozen and only the task-specific heads were fine-tuned on the miospore data. VGG16, a 16-layer network that stacks small 3×3 convolutions, served as a stable, well-understood baseline. ResNet-18, an 18-layer network with residual skip connections that prevent optimization problems in deeper models, offered a lightweight and efficient feature extractor. EfficientNet-B0, the smallest member of the EfficientNet family, provided a compact architecture suited to limited data and compute. During training, images were resized to 224 by 224 pixels and augmented on the fly with flips, rotations of up to 15 degrees, color jitter, normalization, and random erasing, while validation images underwent only resizing and normalization to keep the evaluation unbiased.

The results, averaged over ten independent training runs, showed a clear hierarchy. ResNet-18 achieved the highest mean test accuracy at 87.9 percent, along with a Matthews correlation coefficient of 0.85, a metric that condenses the entire multiclass confusion matrix into a single value between minus one and one and is robust to the class imbalance typical of palynological datasets. VGG16 posted the highest macro-F1 score of 87.3 percent with a comparable test accuracy of 87.5 percent, while EfficientNet-B0 trailed on every metric, with 79.6 percent accuracy, 76.4 percent macro-F1, and an MCC of 0.75. In the best single runs, both ResNet-18 and VGG16 reached a test accuracy of 95.8 percent. The confusion matrices revealed that all three classifiers identified T. torquata flawlessly, with an F1 score of 1.00, likely because that species is large, reaching 85 to 140 micrometers, and bears a distinctive cavate structure, sub-triangular outline, and clear trilete mark. G. lemurata, a cavate spore of roughly 40 to 50 micrometers with a rounded outline, scored between 0.88 and 0.92. S. triangulatus proved hardest, with F1 scores of 0.73 to 0.80, probably because poorer preservation in the Armenian material obscures its diagnostic cone-shaped sculpture and indistinct trilete mark.

The authors are candid about the limits of the proof of concept. The classification dataset of 159 crops is small by deep learning standards, and the heterogeneous “other” class, which lumps together many different taxa, inevitably adds within-class variability. All images were acquired with a single microscope and a single preparation protocol, so performance may shift when the method meets material prepared and imaged under different conditions. Even so, the pipeline’s average per-class accuracies of 84.5 to 86.25 percent fall comfortably within the range of 83.3 to 99.8 percent reported in recent palynological and microfossil classification studies, a notable achievement for fossil material, which is consistently harder to classify than the pristine modern pollen on which most earlier models were trained. Prior work has shown that accuracy drops when models trained on modern pollen are applied to damaged or fossil specimens, making the team’s direct focus on fossil Devonian spores a meaningful step forward.

The researchers are careful to position the tool as an assistant rather than a replacement for expert knowledge. Supervised AI classification remains dependent on expert-defined taxonomic concepts and carefully curated training data, and the workflow is best understood as a screening and decision-support system that standardizes routine identifications, reduces observer bias, and improves reproducibility. The path forward is clear: enlarging the image dataset, broadening taxonomic coverage, incorporating material from additional sections and laboratories, and coupling the detector with automated slide-scanning systems to raise throughput. The code and annotated dataset have been released on Zenodo, inviting the community to build on the work. If the approach scales as hoped, the same two-step logic could extend to other fossil groups, including Paleozoic marine phytoplankton such as acritarchs and prasinophytes, and even scolecodonts, bringing the routine dating of sedimentary rocks, from academic biostratigraphy to oil exploration, into the age of machine perception.

Subject of Research: Automated detection and identification of Devonian miospores using convolutional neural networks

Article Title: Artificial intelligence applied to the automated detection and identification of Devonian miospores

Article References: Lokteva, V., Serobyan, V., Tetard, M., Breuer, P., & Danelian, T. (2026). Artificial intelligence applied to the automated detection and identification of Devonian miospores. Journal of Micropalaeontology, 45(1), 405-413. https://doi.org/10.5194/jm-45-405-2026

Image Credits: AI Generated

DOI: 10.5194/jm-45-405-2026

Keywords: artificial intelligence, convolutional neural networks, Devonian miospores, palynology, biostratigraphy, YOLOv11, ResNet-18, microfossils, upper Frasnian, Armenia, fossil spores, automated microscopy

The system, called Spitz, was developed by a team of forensic scientists at the Civil Police of the Brazilian Federal District in Brasília, working with real casework material rather than curated laboratory samples. At its core is a YOLOv10-L deep neural network, a member of the You Only Look Once family of object detectors that has become one of the most widely used architectures in real-time computer vision. Unlike slower two-stage detectors that first propose candidate regions and then classify them, YOLO models process an entire image in a single pass, predicting bounding boxes and class probabilities simultaneously. That speed matters in forensic settings, where a single microscope slide can contain thousands of fields of view and laboratories face persistent backlogs.

Training the model required a dataset grounded in the messy reality of forensic practice. The researchers assembled 399 microscopic image patches drawn from actual sexual assault cases, all stained with the Christmas Tree stain, the red-and-green histochemical preparation that is a staple of forensic sperm identification. This choice of training data is significant. Staining artifacts, epithelial cells, debris, and variable staining intensity make real casework slides far harder to interpret than idealized reference images, and models trained only on clean laboratory data often falter when confronted with authentic evidence. By anchoring the training set in genuine case material, the Brasília team aimed to build a detector that would hold up under the conditions examiners actually face.

The performance figures reported for the detection model are impressive, though they also reveal the inherent difficulty of the task. Spitz achieved a precision of 94 percent, meaning that when it flags an object as a spermatozoon, it is almost always correct. Recall, the proportion of true sperm cells it manages to find, came in at 72.3 percent, and the mean average precision at a 50 percent intersection-over-union threshold reached 0.842. In practical terms, the system errs on the side of caution: it rarely cries wolf, but it does miss some cells. For forensic work, that trade-off is arguably the right one, because the system is designed not to replace the examiner but to guide them, and a human expert remains in the loop for final verification.

What elevates Spitz beyond a standalone detector is the workflow built around it. The model was integrated into a web interface that supports batch inference across entire slides, reconstructs the spatial layout of the microscope slide, and records the coordinates of every detected sperm cell. An examiner can then navigate the microscope directly to flagged locations for confirmation, rather than sweeping the slide blindly. This coordinate-based verification bridges the gap between automated image analysis and the downstream steps of forensic genetics, where precise localization of sperm cells can feed into techniques such as laser capture microdissection and sperm cell sorting for DNA profiling. The system effectively turns the microscope from a search instrument into a confirmation instrument.

To test whether the technology actually improves forensic practice, the team ran a blind comparative study with three experienced forensic experts who re-examined 30 sexual assault samples using both conventional manual examination and the AI-assisted approach. For each sample, the examiners recorded spermatozoa counts, assigned a classification of Not present, Rare, or Occasional, and logged examination time. The design directly targeted one of the most stubborn problems in forensic biology: inter-examiner variability. Previous research has documented substantial differences in how analysts assess evidence suitability and interpret the same slides, and such variability can affect whether a sample proceeds to DNA extraction at all.

The statistical results favored the AI-assisted workflow across nearly every measure. AI-assisted detection found significantly more spermatozoa than manual examination, and the counts showed lower variability between examiners. Inter-rater agreement, quantified with Fleiss’ Kappa, jumped from 0.698 under manual examination to 0.858 with AI assistance, while raw agreement rose from 73.3 percent to 86.7 percent. The coefficient of variation across classification categories dropped from a mean of 93.5 percent to 80.1 percent, indicating that examiners converged far more closely on how to categorize samples when the algorithm had already flagged candidate cells. Wilcoxon signed-rank tests underpinned these comparisons, lending statistical weight to what the numbers show.

Examination time told a more nuanced story. AI-assisted examinations averaged 6.39 minutes with a standard deviation of 0.59 minutes, compared with 6.93 plus or minus 0.70 minutes for manual examination, a difference that did not reach statistical significance with a p-value of 0.2621. The modest time saving suggests that the primary benefit of Spitz is not speed but quality: more sensitive detection and more reproducible conclusions. The authors also highlight a benefit that is easy to overlook in the statistics: reduced examiner exposure to the intense illumination of the microscope, an occupational burden for analysts who spend their careers hunched over eyepieces.

The study arrives amid a broader wave of machine learning adoption in forensic science. Earlier efforts explored deep convolutional networks for sperm detection on microscope slides, YOLOv5-based sperm cell detection, and automated detection for laser capture microdissection, while commercial platforms have begun offering AI-assisted sperm finding. At the same time, critical reviews of machine learning in forensic DNA profiling have cautioned that validation standards must be rigorous, that datasets are often sensitive and hard to share, and that algorithms must earn the trust of courts and examiners alike. Spitz contributes to this conversation by reporting a validation study built on real casework, blind comparison, and established statistical measures rather than laboratory-only benchmarks.

There are, of course, limits to what the current study demonstrates. The annotated dataset and model weights cannot be released publicly because the underlying images come from sensitive forensic casework protected by institutional data policy, although the code for the Spitz interface may be shared on reasonable request. The recall of 72.3 percent means the algorithm alone is not exhaustive, which is precisely why the system is framed as an examiner’s assistant rather than an autonomous judge. Still, the direction of travel is clear. A model trained on 399 image patches from genuine cases, wrapped in a workflow that respects the expertise of human analysts, outperformed those same analysts on sensitivity and consistency in a blind trial. For a field where the stakes are measured in justice delivered or denied, that combination of technological ambition and procedural humility may prove to be the most important finding of all.

Subject of Research: Deep learning-based automated detection of spermatozoa in forensic microscopy for sexual assault investigations

Article Title: Spitz: a YOLO-based deep learning system for automated spermatozoa detection in forensic genetic analysis

Article References: Humanes, A. C., Calil, A. L. A., Wawruk, H. D., Carneiro, L. D., Silveira, K. B., Yen, W. C., de Andrade Gomes, J., & Meirelles, A. L. S. (2026). Spitz: a YOLO-based deep learning system for automated spermatozoa detection in forensic genetic analysis. International Journal of Legal Medicine. https://doi.org/10.1007/s00414-026-04013-7

Image Credits: AI Generated

DOI: 10.1007/s00414-026-04013-7

Keywords: forensic genetics, deep learning, YOLOv10, spermatozoa detection, sexual assault evidence, automated microscopy, inter-rater agreement, Christmas Tree stain, object detection, DNA profiling, forensic science, computer vision