Evidence map›Paper›PMID 41646653›Full record

ArticleFrontiers in behavioral neuroscience2025

Smart Lids for deep multi-animal phenotyping in standard home cages.

Sead Delalić, Michael Kaca, Pratomo Alimsijah, Noah Weber, Elmedin Selmanović, Mikailynn Galindez, Glen Marquez, Francisco Balmaceda, Eldina Delalić, Iman Bekkaye and 6 more

Abstract read
In one paragraph

Article in Frontiers in behavioral neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Effects of Cage Change and Nest Transfer on Artificial Intelligence-Measured Behaviors in Male C57BL/6 Mice.Journal of the American Association for Laboratory Animal Science : JAALAS · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Sead Delalić *Olden Labs PBC, South San Francisco, CA, United States.
Michael Kaca *Olden Labs PBC, South San Francisco, CA, United States.
Pratomo Alimsijah *Olden Labs PBC, South San Francisco, CA, United States.
Noah Weber *Olden Labs PBC, South San Francisco, CA, United States.
Elmedin SelmanovićOlden Labs PBC, South San Francisco, CA, United States.
Mikailynn GalindezOlden Labs PBC, South San Francisco, CA, United States.
Glen MarquezOlden Labs PBC, South San Francisco, CA, United States.
Francisco BalmacedaOlden Labs PBC, South San Francisco, CA, United States.
Eldina DelalićOlden Labs PBC, South San Francisco, CA, United States.
Iman BekkayeNext IT d.o.o, Sarajevo, Bosnia and Herzegovina.
Lejla BakijaNext IT d.o.o, Sarajevo, Bosnia and Herzegovina.
Meliha Kurtagić-PašalićNext IT d.o.o, Sarajevo, Bosnia and Herzegovina.
Esma AgićNext IT d.o.o, Sarajevo, Bosnia and Herzegovina.
David Anderson *Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, United States.
Amy Wagers *Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, United States.
Michael Florea *Olden Labs PBC, South San Francisco, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The reproducibility crisis and translational gap in preclinical research underscore the need for more accurate and reliable methods of health monitoring in animal models. Manual testing is labor-intensive, low-throughput, prone to human bias, and often stressful for animals. Although many smart cages have been introduced, they have seen limited adoption due to either low throughput (being limited to single animals), low data density (a few metrics only), high costs, a need for new space or infrastructure in the vivarium, high complexity use, or a combination of the above. Although technologies for video-based single-animal tracking have matured, no existing technology enables robust and accurate multi-animal tracking in standard home cages. To solve these problems, we built a new type of assay device: the Smart Lid. Smart Lids mount to existing racks, above standard home cages and stream video and audio data, turning regular racks into high-throughput monitoring platforms. To solve the multi-animal tracking problem, we developed a new computer vision pipeline (MOT - Multi-Organism Tracker) along with a new ear tag purpose-designed for computer vision tracking. MOT achieves over 97% accuracy in multi-animal tracking while maintaining an affordable runtime cost (less than $100 per month). The pipeline returns 21 health-related metrics, covering activity, feeding, drinking, rearing, climbing, fighting, cage positioning, social interactions and sleeping, with additional metrics under development.

Indexed as

3RAIanimal welfarecomputer visionhome-cage monitoringmulti-animal trackingphenomicsphenotyping

Identifiers

PMID41646653
PMCPMC12869192

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.