PPCE: Preparticipation Cardiovascular Examination

• Preparticipation Cardiovascular Examination (PPCE) is a systematic protocol that identifies athletes at risk for sudden cardiac death by integrating clinical assessments, anthropometric measurements, and digital ECG screening.
• Automated methods using deep learning enable accurate, sub-centimeter anthropometric measurements and improve digital ECG sensitivity and specificity for detecting structural and electrical abnormalities.
• The integration of AI-driven modules in PPCE enhances throughput, reduces screening costs, and minimizes operator bias, although further validation in real-world conditions is required.

Preparticipation Cardiovascular Examination (PPCE) is a systematic, protocolized approach for identifying individuals—primarily athletes—who are at elevated risk for sudden cardiac death (SCD) due to structural or electrical cardiac abnormalities. PPCE is focused on early detection through integrated clinical assessment, targeted anthropometric measurements, and, in advanced practice, digital diagnostics including electrocardiographic (ECG) analysis and deep learning–based anthropometry. The PPCE process continues to evolve, incorporating high-throughput, automated methods for risk stratification, large-scale screening, and regulatory compliance, as demonstrated in recent literature (Mareque et al., 6 Dec 2025, Xiang et al., 2 Dec 2024).

1. Core Objectives and Risk Stratification in PPCE

PPCE is structured to preempt the catastrophic sequelae of undiagnosed cardiac disease—chiefly SCD—by deploying a suite of diagnostic tools intended to uncover occult structural (e.g., hypertrophic cardiomyopathy, aortic root dilation) and electrical (e.g., long QT syndrome, arrhythmogenic right ventricular cardiomyopathy) pathology. The protocol typically integrates:

• Anthropometric measurement to detect syndromic or disproportional growth patterns indicating connective tissue or metabolic disorders (e.g., Marfan syndrome, central obesity).
• Electrocardiographic screening, which ranges from 14-point physical examination (AHA PPE) to 12-lead ECGs as per International Olympic Committee guidelines and, more recently, smartwatch-based digital protocols.
• History and symptom inventory remains part of the baseline approach but has low sensitivity for SCD risk.

The risk stratification process leverages threshold-based flagging. For example, arm span/height >1.05, waist circumference above WHO thresholds (≥94 cm for men, ≥80 cm for women), or waist-to-hip ratio (WHR) >0.9 in men, >0.85 in women are all metrics triggering further workup (Mareque et al., 6 Dec 2025).

2. Anthropometric Measurement: Automated Deep Learning Approaches

Historically, anthropometry has required manual caliper and tape measurements—prone to inter-operator variability and inefficiency at scale. Automation via deep learning breaks this bottleneck by extracting measurements from 2D images, as described by the following data-driven protocol (Mareque et al., 6 Dec 2025):

• Data Genesis: 100,000 synthetic 2D images (50,000 per sex) are generated from 3D body meshes (SMPL model), rendered in T-pose under constant illumination, with image formation approximated as $u = sX + t_x$, $v = sY + t_y$.
• Backbone Architectures: VGG19 (16 conv, 3 FC), ResNet50 (50 layers, skip connections), DenseNet121 (121 layers, dense connections) with frozen feature extractors and custom regression head (Flatten $\to$ FC(1024) $\to$ BN $\to$ ReLU $\to$ FC(512) $\to$ BN $\to$ ReLU $\to$ FC(128) $\to$ BN $\to$ ReLU $\to$ Output(16)).
• Key Targeted Measurements:
  • Shoulder-to-wrist (arm length)—screening for marfanoid habitus.
  • Torso length—identification of skeletal dysplasias and thoracic mechanics.
  • Waist and pelvis circumference (risk and distribution markers for metabolic syndrome).
  • Leg length—supporting disproportion/marfanoid syndromic profiling.
• Training Regimen: 70k/15k/15k train/validation/test split, Adam optimizer (lr = $1 \times 10^{-4}$), batch size 350, early stopping, ImageNet normalization.
• Performance: Mean MAE for ResNet50: 0.668 cm (shoulder-wrist: 0.361 cm, torso: 0.451 cm, waist: 1.359 cm, pelvis: 1.157 cm, leg: 0.430 cm). Sub-centimeter accuracy places ResNet50 above Conv-BoDiEs baselines for arm and torso.

Table: Selected Measurement Performance (MAE, cm)

Measurement	ResNet50 MAE	VGG19 MAE	DenseNet121 MAE
Shoulder-wrist	0.361	—	—
Torso length	0.451	—	—
Waist circumference	1.359	—	—

Manual measurement variability and subjective assessment are eliminated by this automated pipeline.

3. Digital ECG and AI-Driven Electrophysiological Screening

Modern PPCE workflows increasingly incorporate digital ECG acquisition via portable, consumer devices, notably smartwatches, and leverage advanced machine learning for both signal synthesis and risk classification (Xiang et al., 2 Dec 2024):

• Acquisition Protocol: Apple Watch Series 7 (sampling at 250 Hz), using sequential 4-lead positioning (Lead II, aVR, V2, V5) with specific electrode contact points.
• Preprocessing: R-peak detection, Butterworth filtering, min-max rescaling, FFT downsampling to 100 Hz; Z-score normalization.
• 4-to-12 Lead Upscaling: Each of the eight unmeasured leads ($I, aVL, aVF, V1, V3, V4, V6, III$) is synthesized by cubic regression using the four measured signals and refined through level-L Daubechies db4 wavelet decomposition (training with $L_1$ regularization via Adam; MAPE <5% for most leads).
• TAES Classifier: Transformer Auto-Encoder System comprising a 4-layer multi-head Transformer encoder/decoder ($d_{model}=64$, $h=3$ heads) and a 5-layer symmetric Conv2D auto-encoder for representation learning. Latent features are classified using one-versus-one SVM with RBF kernel.
• Metrics: Sensitivity (Se) = 95.3%, Specificity (Sp) = 99.1%, Macro-F1 = 0.95, Micro-F1 = 0.97 (test set).

The CSS (Comprehensive Screening System) achieves rapid, high-throughput, and physician-independent cardiac screening which can surpass the specificity and sensitivity of both the AHA 14-point PPE and traditional physician-interpreted 12-lead ECG (Xiang et al., 2 Dec 2024).

4. PPCE Workflow Integration and Deployment Considerations

The integration of automated anthropometry and smartwatch ECG into PPCE modifies operational workflows:

• Anthropometric module: Single controlled-frontal photograph per athlete, processed on GPU or CPU (≤ 50 ms/image and ≤ 200 ms/image, respectively), fully operator-independent. Participating athletes are screened en masse with large throughput (hundreds/hour).
• CSS-Electrocardiography: Technician guides the subject through a 4-lead Apple Watch protocol (4 minutes total). Data are securely uploaded for preprocessing, upscaling, TAES-based classification, and reporting—total processing per subject <2 minutes.
• Decision Support: Both modalities auto-flag subjects above anthropometric or electrophysiological risk thresholds, prompting immediate referral for echocardiography or genetics consult as indicated.
• Automation: Eliminates manual data entry, measurement bias, and operator dependence, suitable for schools and club-based mass screening.

Table: Comparative Throughput and Diagnostic Performance

Method	Sensitivity (%)	Specificity (%)	Throughput	Operator
AHA 14-point PPE	18.8	68.0	Low	Physician
Standard 12-lead ECG	≈94	≈93	Low	Physician
Apple Watch CSS	97.3	99.1	High (4 min/athlete)	Technician/GPU

5. Limitations, Regulatory, and Compliance Framework

While these automated digital methods optimize scale and reproducibility, current limitations and regulatory requirements include:

• All reported accuracy and reliability data for anthropometry are evaluated on synthetic, standardized images; translation to in-field conditions (clothing, variable poses, lighting) is pending. Key next steps include fine-tuning on annotated real-world photographs and augmentation to simulate non-ideal conditions (Mareque et al., 6 Dec 2025).
• The CSS classifier presently targets five SCD-associated phenotypes—extension to additional arrhythmias will require expansion of the labeled database and retraining (Xiang et al., 2 Dec 2024).
• Initial validation cohorts skew demographically; multicenter, racially and gender-balanced studies are required for downstream generalization.
• All automated modules used in diagnostic pathways must meet FDA 510(k), CE-mark, and privacy compliance (HIPAA/GDPR for identifiable data).
• Robust technician training is essential to ensure correct device placement and data quality, especially for ECG acquisition.

6. Quantitative Impact and Future Directions

Embedding deep learning–driven anthropometric and digital ECG modules into PPCE protocols directly addresses logistical, technical, and economic barriers to population-scale cardiac screening (Mareque et al., 6 Dec 2025, Xiang et al., 2 Dec 2024). Notable impacts include:

• Cost reduction: Automated image-based anthropometry and smartwatch acquisition (device cost $399) radically reduce per-athlete screening expense compared to standard 12-lead ECG setups.
• Throughput: Both anthropometric and CSS-electrocardiographic modules operate at scale, processing hundreds of athletes per hour, with rapid turnaround for clinical decision-making.
• Diagnostic accuracy: Automated systems deliver sub-centimeter measurement accuracy (anthropometry) and >95% sensitivity/specificity (CSS), surpassing existing manual and non-automated protocols.

Future directions include fine-tuning and multicenter validation of anthropometric models on real, annotated images, augmentation for realistic setting robustness, data and model expansion for rare phenotypes, and technological harmonization under evolving regulatory standards.

7. PPCE Evolution and the Role of Automation

The evolution of PPCE is being shaped by the integration of AI-driven measurement and classification pipelines. Fully automated pipelines support operator-independent screening, reproducible risk stratification, and cost-effective deployment in both resource-rich and constrained environments. Rigorous validation, regulatory compliance, and technical augmentation for real-world complexity remain critical to sustainable implementation. As these technologies mature, PPCE will increasingly align with its primary goal: scalable, early identification and mitigation of SCD risk among young athletes (Mareque et al., 6 Dec 2025, Xiang et al., 2 Dec 2024).