2nd/10_Wiki/Topic_Programming/AI_and_ML/Prenatal-Neurology.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-prenatal-neurology	Prenatal Neurology	10_Wiki/Topics	verified	self	Fetal Neurology Prenatal Neuroscience	none	A	0.85	applied	neurology fetal-medicine neurodevelopment medical-imaging		2026-05-10	pending	language framework Python MONAI / nnU-Net / 3D Slicer

Prenatal Neurology

매 한 줄

"매 fetal nervous system 의 development, imaging, anomaly detection — neural tube 부터 birth 까지". 1980s ultrasound 의 advent 로 시작, 2010s fetal MRI 로 detail 폭증, 2020s deep learning 으로 automated segmentation/screening. 2026 currently SVRTK + diffusion priors 로 motion-corrected fetal MRI volumes 를 minutes 안에.

매 핵심

매 developmental milestones

• Week 3-4: neural plate → neural tube closure. Failure → spina bifida, anencephaly.
• Week 5-7: 3 primary vesicles → 5 secondary (telencephalon, diencephalon, mesenc, metenc, myelenc).
• Week 8-16: neuronal proliferation in ventricular zone.
• Week 12-22: neuronal migration along radial glia. Failure → lissencephaly, heterotopia.
• Week 22-40: gyrification, cortical organization, myelination begins.

매 imaging modalities

• Ultrasound (US): routine 18-22 wk anatomy scan; transvaginal early.
• Fetal MRI: T2-HASTE / SSFSE; problem-solving when US ambiguous.
• Doppler: middle cerebral artery flow (anemia, hypoxia).
• Fetal MEG / EEG: research only.

매 common anomalies

1. Neural tube defects (NTDs): spina bifida, anencephaly. Folate-preventable.
2. Ventriculomegaly: atrial width >10mm.
3. Corpus callosum agenesis: 1:4000.
4. Posterior fossa: Dandy-Walker, Blake's pouch cyst.
5. Cortical malformations: lissencephaly, polymicrogyria.
6. TORCH infections: CMV, Zika → microcephaly, calcifications.

매 AI in fetal imaging (2024-2026)

• SVRTK / NiftyMIC: slice-to-volume reconstruction from motion-corrupted MRI.
• nnU-Net fetal: automatic brain extraction + tissue segmentation.
• dHCP atlas: developing Human Connectome Project — gestational-age-specific atlas.
• Diffusion priors: latent diffusion models for fetal MRI super-resolution (2024-2025).
• Automated biometry: BPD, HC, AC, FL from US in real time (e.g., Caption Health-style).

💻 패턴

Fetal brain extraction (nnU-Net)

# Train on FeTA Challenge dataset (gestational ages 20-35 wk)
# nnU-Net handles preprocessing, augmentation, ensemble
import subprocess
subprocess.run([
    "nnUNetv2_train", "Dataset080_FetalBrain", "3d_fullres", "0",
    "--npz",
])
# Inference
subprocess.run([
    "nnUNetv2_predict",
    "-i", "input_dir", "-o", "output_dir",
    "-d", "080", "-c", "3d_fullres", "-f", "0",
])

Slice-to-volume reconstruction (SVRTK)

# Motion-corrupted T2 stacks → isotropic 3D volume
mirtk reconstruct recon.nii.gz \
  4 stack_axi.nii.gz stack_cor.nii.gz stack_sag.nii.gz stack_obl.nii.gz \
  -mask brain_mask.nii.gz \
  -resolution 0.5 \
  -iterations 3 \
  -thickness 3.0 3.0 3.0 3.0

Tissue segmentation w/ MONAI

import torch
from monai.networks.nets import SwinUNETR
from monai.transforms import Compose, LoadImaged, NormalizeIntensityd, EnsureChannelFirstd

model = SwinUNETR(img_size=(96, 96, 96), in_channels=1, out_channels=8,
                  feature_size=48, use_checkpoint=True)
model.load_state_dict(torch.load("feta_swinunetr.pt"))
# Outputs: CSF, GM, WM, ventricles, cerebellum, brainstem, deep GM, hippocampus

Gestational-age-specific atlas registration

# dHCP: 36 atlases from 28-44 weeks PMA
import ants
fixed = ants.image_read(f"dhcp_atlas/week_{ga_weeks}.nii.gz")
moving = ants.image_read("fetal_brain_recon.nii.gz")
reg = ants.registration(fixed, moving, type_of_transform="SyN")
warped = reg["warpedmovout"]

Automated US biometry (real-time)

# YOLOv8 finds standard plane → keypoint regression for BPD/HC/AC/FL
from ultralytics import YOLO
plane_model = YOLO("us_plane_classifier.pt")
biometry = YOLO("us_keypoints.pt")
res = plane_model(frame)
if res[0].names[res[0].probs.top1] == "axial_thalami":
    pts = biometry(frame)[0].keypoints
    bpd_mm = euclidean(pts[0], pts[1]) * pixel_spacing

Cortical folding metric (gyrification index)

# GI = total surface area / convex hull area (per hemisphere)
import nibabel as nib, numpy as np
from skimage.measure import marching_cubes, mesh_surface_area
seg = nib.load("cortex.nii.gz").get_fdata() > 0
verts, faces, _, _ = marching_cubes(seg, level=0.5)
surf = mesh_surface_area(verts, faces)
# Convex hull surface
from scipy.spatial import ConvexHull
hull = ConvexHull(verts)
gi = surf / hull.area

매 결정 기준

상황	Approach
Routine screening 18-22 wk	Ultrasound (anatomy scan)
Suspected CNS anomaly on US	Fetal MRI (32-34 wk optimal)
Motion-corrupted MRI	SVRTK reconstruction
Quantitative volumetry	dHCP atlas + nnU-Net
Suspected NTD	High-resolution US + AFP + acetylcholinesterase

기본값: US first; MRI for problem-solving; AI segmentation for research/quantitative endpoints.

🔗 Graph

🤖 LLM 활용

언제: fetal imaging analysis, neurodevelopmental research, congenital anomaly screening pipelines. 언제 X: clinical diagnosis without licensed clinician — AI augments, never replaces.

❌ 안티패턴

• Adult MRI tools on fetal data: gestational-age-specific contrast / atlas required.
• Ignoring motion artifact: fetal motion → reconstruct first.
• No GA stratification: 24wk vs 36wk brain are different organs.
• Single-modality conclusion: combine US + MRI + genetics.
• Overcalling ventriculomegaly: 10-12mm often resolves; counsel carefully.

🧪 검증 / 중복

• Verified (FeTA Challenge MICCAI, dHCP, ISUOG guidelines, AIUM practice parameters).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — fetal neurodevelopment + AI imaging stack