As of mid-2026 no deep-network critical-period model has been shown to quantitatively match a human critical-period window — the biology→DL→clinic loop remains open

The founding deep-network critical-period result validates against animal data only and by explicit admission lacked the human data to do otherwise (claim-founding-dnn-critical-period-paper-validated-on-animal-data-only). A 2026-07-14 search for follow-on work that would close the gap — a deep-network model whose critical-period timing is fit to or predicts a human window such as the amblyopia treatment window — surfaced no positive instance.

Two recent papers extending the Achille et al. framework were read in full. "One Period to Rule Them All" (Fukase et al. 2025, arXiv:2506.15954) stays entirely inside DNN-internal analysis (layer rotations, generalization) and does not mention amblyopia or human clinical timing. "Learning to See Through a Baby's Eyes" (Cai, Lin, Nunna & Zhang, arXiv:2511.14440) discusses human infant and cataract-removal visual development qualitatively — "Children who begin visual experience with relatively high acuity due to early cataract removal can discriminate faces based on local features but fail to detect their configural changes" (§5.1) — but performs no quantitative comparison between its model's critical-period timing and a human clinical window. Neither reproduces a match against human amblyopia treatment-window data.

[unverified — could not confirm a positive instance after search on 2026-07-14; strong primary-source evidence that the founding paper deliberately did not attempt the human match, citing insufficient human data]. This is scoped to what the search surfaced, not a proof that no such work exists anywhere; the claim is a state-of-the-literature finding, appropriately provisional. The nearest candidate not fully read is the Project Prakash / Sinha-lab work (Vogelsang et al. 2024, Science), which pairs a DNN with real late-sight-restoration patient data — but on color-cue reliance, not critical-period timing; whether it fits any timing parameter is the specific unclosed check.

The open verification lives at question-deep-net-critical-period-predicts-human-amblyopia-timing. This note answers the "why not yet" half — the founding paper explains the gap as a data problem — while leaving the existence question genuinely open. It keeps the human extension of claim-deep-nets-have-critical-learning-periods-timed-like-animals and the deflationary reading of claim-critical-periods-arise-from-information-plasticity-not-biology marked as analogy-not-demonstration until a real human timing match is shown.