Artificial intelligence models for clinical prediction are proliferating across neurosurgery, but their aggregate discriminative performance, distribution along the care pathway and methodological quality are unclear. We systematically reviewed and meta-analysed these studies in neurosurgery and its subspecialities along with their risk of bias appraisal. We performed a PRISMA search for peer-reviewed original studies using AI to predict a clinical outcome in neurosurgical populations. Metrics were pooled using random effects modelling. Each study was classified as diagnostic, prognostic or forecasting and was assigned an evidence level. Clinical prediction task sub-stratification analysis was performed. Risk-of-bias was assessed using PROBAST. 1063 studies were included. Across 451 studies reporting AUC, pooled discrimination was 0.86, ranging from 0.84 (spine) to 0.87 (neuro-oncology), with extreme heterogeneity. Performance varied by clinical pathway stage, highest for diagnosis, grading and molecular tasks and lowest for post-operative tasks. Only 51 studies were using prospective data with external validation, whereas 619 rested on internal validation alone or no validation. 89 percent studies were at high risk-of-bias, driven by the analysis domain. AI models report high performance across all the neurosurgical subspecialities, concordant across AUC, accuracy, sensitivity and specificity and robust to risk-of-bias stratification. Performance is highest for imaging-anchored diagnostic tasks and lowest at decision-facing post-operative stage. However, the literature is dominated by optimism-prone, internally-validated and poorly calibrated models with extreme heterogeneity. External validation, calibration analysis and reporting guidelines adherence remain prerequisites for clinical adoption.
We introduce nanopath, an open-source framework and benchmark for rapidly training and evaluating small-scale pathology foundation models. Using a single H100 GPU, a typical nanopath model trains in a little over an hour on up to one million TCGA pathology tile presentations and evaluates within 25 minutes on a broad suite of tile-level and slide-level tasks. Nanopath enables rapid exploration of research ideas at small scale, so promising training recipes can subsequently be tested with more data and compute. To enable fair comparisons between ideas, we constrain submissions to fixed data and compute budgets while keeping the downstream evaluation protocol fixed. Anyone may clone the codebase, automatically download the data, reproduce or modify existing models, and submit a run to Labless, our public experiment ledger where researchers and AI agents can search through prior attempts. Since its public release in June 2026, nanopath has received 1,269 experiments from 20 contributors. Here, we detail its design and assess how well its rapid evaluation tracks standardized THUNDER, HEST, and PathoBench (CPTAC) benchmarks across six nanopath and 14 reference foundation models. Nanopath's fast evaluation suite strongly tracked these standardized benchmarks, and a few 22M parameter nanopath models even outperformed giant 1.1B parameter models on a subset of downstream tasks. We subsequently discuss how we have since optimized nanopath's evaluation suite to be an even more faithful, quick proxy for external benchmarking. Nanopath provides an accessible testbed for students, researchers, and AI agents to explore pathology foundation modeling through fair, transparent, and directly comparable experiments.
Reconstructing topologically complete, sub-voxel-accurate 3D bilateral coronary artery trees from cardiac CT angiography (CCTA) requires simultaneously recovering continuous centerline trajectories and separating dense left/right coronary territories without cross-ostial collisions. We present a five-stage tubular drift and ipsilateral territory decoupling framework (M1-M5). Stage M1 pairs a 398,843-parameter multi-scale 3D pyramid encoder with a four-head tubular field network predicting vesselness probability, signed tubular boundary distance, unit tangent vectors, and continuous 3D centripetal displacement vectors. Stage M2 introduces recall-preserving multi-task optimization with 7-group AdamW parameter decoupling, L2-SP backbone regularization, and per-step anchor shrinkage. Stage M3 calibrates cylindrical Valsalva-sinotubular-junction wall coordinates and native-resolution orifice contact expectations for robust left and right ostial rooting. Stage M4 constructs a scalar-potential-weighted minimum-cost arborescence followed by normal-plane cross-sectional pullback and centripetal trajectory pruning. Stage M5 enforces coverage-guarded ipsilateral-field-coupled bilateral collision reduction and subtree handover. Across a 14-case held-out development cohort (Dev-14, 17-arm forward incremental build and 9-arm leave-one-out ablation) and an independent 50-case out-of-sample evaluation cohort (OOS-50) benchmarked against seven external architectures (nnU-Net, 3D UX-Net, Swin-UNETR, MedNeXt, CS2-Net, clDice-UNet, and Point-UNet), M1-M5 reduces bilateral chamfer distance on Dev-14 from 3.4381 mm to 2.2759 mm and achieves 2.7671 mm on OOS-50 with 7.11% bilateral collision rate and 79.7% distal branch completeness.
Oral squamous cell carcinoma OSCC is a major global health burden, particularly in several regions across Asia, Africa, and South America, where it accounts for a significant proportion of cancer cases. Early detection dramatically improves outcomes, with stage I cancers achieving up to 90 percent survival. However, traditional diagnosis based on histopathology has limited accessibility in low-resource settings because it is invasive, resource-intensive, and reliant on expert pathologists. On the other hand, oral cytology of brush biopsy offers a minimally invasive and lower cost alternative, provided that the remaining challenges, inter observer variability and unavailability of expert pathologists can be addressed using artificial intelligence. Development and validation of robust AI solutions requires access to large, labeled, and multi-source datasets to train high capacity models that generalize across domain shifts. We introduce the first large and multicenter oral cytology dataset, comprising annotated slides stained with Papanicolaou(PAP) and May-Grunwald-Giemsa(MGG) protocols, collected from ten tertiary medical centers in India. The dataset is labeled and annotated by expert pathologists for cellular anomaly classification and detection, is designed to advance AI driven diagnostic methods. By filling the gap in publicly available oral cytology datasets, this resource aims to enhance automated detection, reduce diagnostic errors, and improve early OSCC diagnosis in resource-constrained settings, ultimately contributing to reduced mortality and better patient outcomes worldwide.