pockethb-base

base hemoglobin regressor from the pocketHb project. not a measurement tool. read this whole card before using.

what this is

a global, population-level Hb regressor on fingernail photos. methodology follows Rudokaite et al., BNAIC 2025 (Tilburg/Sanquin smartphone-Hb paper):

• frozen convnext_tiny.fb_in22k_ft_in1k via imm (no fine-tuning)
• Shades-of-Gray (p=6) illumination correction → 224x224 resize → ImageNet normalize
• per-patient aggregation: mean + std of crop embeddings (1536-d patient vector)
• standardize → PLS + SVR(RBF) → isotonic calibration → weighted blend
• trained on Nature Sci Data 2024 fingernail+Hb dataset (n=250 subjects, single CBC each)

trained on nail crops only. the public Nature 2024 release ships 600x800 images, but 606 of 750 skin bboxes were labelled in a taller source frame and now sit below the bottom edge of the released images. only 83 patients would survive a nail+skin fusion — we ran nail-only on the full 250 instead.

metrics (5-fold stratified CV, n=250)

• MAE: 2.09 g/dL
• RMSE: 2.74 g/dL
• R²: -0.05

R² ≈ 0 means this global model is essentially predicting near the dataset mean. it barely beats the predict-mean floor (MAE ~2.14 g/dL) on this dataset. this is the documented ceiling of population-level Hb modeling at sub-1000-subject scale, not a bug:

• The Rudokaite/BNAIC paper itself, with the same methodology on n=159, got MAE 0.6 mmol/L — that sounds clinical until you notice their predict-mean floor was ~0.63 mmol/L. similar R² ≈ 0 story, smaller absolute number because their donor population had Hb std 0.79 mmol/L vs our 2.67 g/dL.
• Mannino et al. (PNAS 2025) only got clinical-near accuracy with n=9,061 subjects PLUS per-user personalization. their global model alone is not clinically useful either; they patented and locked the personalization layer (US 12268498).

intended use

only as an input to a per-user calibration / personalization layer. the open-source contribution of pocketHb is exactly that personalization layer. without it, this base regressor is research artifact only.

not a medical device

research replication only. must not be used to estimate anyone's actual hemoglobin in any clinical, diagnostic, or treatment context. not FDA cleared. not validated clinically. not a doctor. if you need a hemoglobin reading, get a blood test.

how to use

`python import pickle from huggingface_hub import hf_hub_download

download bundle

bundle_path = hf_hub_download(repo_id='bubbaonbubba/pockethb-base', filename='pockethb_base.pkl') with open(bundle_path, 'rb') as f: bundle = pickle.load(f)

bundle contains: backbone_name, image_size, imagenet_mean/std,

shades_of_gray_p, modalities, aggregation, blender, n_train_patients, seed

`

see the pocketHb repo for the full inference pipeline including image preprocessing and embedding extraction.

citation context

• Mannino et al., PNAS 2025. doi:10.1073/pnas.2424677122
• Rudokaite et al., BNAIC 2025 (Tilburg/Sanquin)
• Nature Sci Data 2024 dataset: doi:10.1038/s41597-024-03895-9

license

MIT.