⚡ Quick Summary

This systematic review evaluated the application of artificial intelligence (AI) in detecting obstructive sleep apnea (OSA) using clinical and demographic data. The findings indicate that AI models can significantly enhance detection capabilities, with AUC values ranging from 0.62 to 0.93, although methodological limitations remain a challenge.

🔍 Key Details

• 📊 Dataset: 447 records reviewed, 26 studies met inclusion criteria
• 🧩 Features used: Age, BMI, neck circumference, comorbidities
• ⚙️ Technology: Decision trees, support vector machines, neural networks
• 🏆 Performance: AUC values mostly exceeding 0.80

🔑 Key Takeaways

• 📈 AI models show promise in improving OSA detection.
• 🔍 Methodological quality varied significantly across studies.
• 📉 AUC values ranged from 0.62 to 0.93, with most studies achieving >0.80.
• 📅 Research output increased notably from 2021 to 2024.
• ⚠️ Limitations include methodological heterogeneity and lack of external validation.
• 🌍 Future studies should focus on diverse populations and standardized reporting.

📚 Background

Obstructive sleep apnea (OSA) is a prevalent sleep disorder characterized by repeated interruptions in breathing during sleep. Traditional diagnostic methods, such as polysomnography, can be resource-intensive and inconvenient. The integration of artificial intelligence into the diagnostic process offers a promising alternative, potentially allowing for more accessible and efficient detection based on readily available clinical and demographic data.

🗒️ Study

This systematic review adhered to PRISMA guidelines and aimed to assess the effectiveness of AI models in detecting or stratifying OSA. The review included studies published between 2014 and 2024 that utilized clinical and demographic data, validated against polysomnography or cardiorespiratory polygraphy, and reported performance metrics such as the area under the curve (AUC).

📈 Results

Out of 447 records screened, 26 studies met the inclusion criteria. The most common algorithms employed were decision trees, support vector machines, and neural networks. The AUC values reported in these studies ranged from 0.62 to 0.93, with a majority exceeding 0.80. However, the review highlighted issues such as methodological heterogeneity and the exclusion of incomplete cases, which limited the comparability of results.

🌍 Impact and Implications

The findings from this review suggest that AI has the potential to significantly enhance the detection of OSA, which could lead to improved patient outcomes and more efficient healthcare delivery. However, the methodological limitations identified underscore the need for further research that prioritizes external validation and diverse populations. By addressing these gaps, the clinical translation of AI in OSA detection could become a reality, ultimately benefiting a broader range of patients.

🔮 Conclusion

This systematic review highlights the potential of AI models in improving the detection of obstructive sleep apnea. While the results are promising, the identified methodological limitations must be addressed in future research. By focusing on external validation and adherence to standardized reporting frameworks, we can pave the way for the successful integration of AI technologies into clinical practice for OSA detection.

💬 Your comments

What are your thoughts on the integration of AI in detecting obstructive sleep apnea? We would love to hear your insights! 💬 Leave your comments below or connect with us on social media:

• 🐦 X
• 📘 Facebook
• 🔗 LinkedIn