Deep learning-based multi-level airway collapse site localization on obstructive sleep apnea patients based on snoring sounds

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: We're looking at the paper: "Deep learning-based multi-level airway collapse site localization on obstructive sleep apnea patients based on snoring sounds".

Jane: Here's the gist of the paper. The gist The study investigated deep learning models to identify multi-level upper airway collapses in obstructive sleep apnea patients using snoring sounds, finding that Audio Spectrogram Transformer (AST) slightly outperformed ResNet-50 in identifying V and O level obstructions, while both models struggled with T, E, and RG classifications due to limited data.

Tom: Data Collection and Labeling The researchers used snoring recordings from 37 subjects undergoing drug-induced sleep endoscopy (DISE) between 2020 and 2021 to train their models<ref:2408.16030#pg6>.

Jane: Snoring sounds were labeled according to the VOTE (Velum, Orophar-ynx, Tongue Base, Epiglottis) classification system, resulting in specific counts for each class including 403 O and 77 T labels<ref:2408.16030#pg6>.

Tom: The data preparation involved synchronizing endoscopic video tapes with breath sounds and aligning the snoring labels (Set A) and VOTE labels (Set B)<ref:2408.16030#pg9>.

Paper summary: Tom: So we’re looking at this paper today called "Deep learning-based multi-level airway collapse site localization on obstructive sleep apnea patients based on snoring sounds." It tackles identifying where those collapses are happening in people with sleep apnea using just snoring sounds.

Jane: That’s the main goal, Tom. The study uses deep learning models, specifically ResNet-fifty and an Audio Spectrogram Transformer called AST, to pinpoint multi-level upper airway collapses from snoring recordings of patients with obstructive sleep apnea <ref:2408.16030#pg3,multi-level upper airway collapses>.

Lu: What’s interesting about this approach is that they are training these models on data gathered from drug-induced sleep endoscopy, or DISE, which gives them a pretty direct look at the airways.

Meng: So what do they claim? Basically, they fine-tuned ResNet-fifty and AST to classify obstructions at four VOTE levels: Velum, Oropharynx, Tongue Base, and Epiglottis <ref:2408.16030#pg3>. They also looked for retropalatal and retroglossal regions.

Jane: And the results show that the Audio Spectrogram Transformer model actually outperformed ResNet-fifty in identifying obstructions at the Velum level with an F1-score of zero point seven one compared to zero point six four, and it also did better for Oropharynx obstructions with an F1-score of zero point eight zero versus a zero point seven two for ResNet-fifty <ref:2408.16030#pg3>.

Tom: That’s a solid performance gap on the V and O levels, which is encouraging because those are often considered key areas for OSA assessment in snoring analysis. But then they hit a wall with the Tongue Base and Epiglottis classifications <ref:2408.16030#pg3>.

Lu: They did struggle there, which makes sense considering the data limitations; they mentioned that both models struggled with T, E, and RG classifications because of small sample sizes even after using synthetic minority over-sampling technique.

Jane: So, the study is saying that while AST is better than ResNet-fifty for V and O levels—reaching an AUC of zero point eight nine for Velum obstructions <ref:2408.16030#pg3>—the other levels like Tongue Base, Epiglottis, and the RP or RG regions remain difficult to classify reliably right now.

Meng: From a deployment standpoint, that limitation is huge. If we can’t accurately pinpoint those lower levels with the current data size, it means we can't build a comprehensive diagnostic tool yet for all parts of the airway using just these sounds <ref:2408.16030#pg3>.

Tom: Exactly. But the paper doesn't stop there; they also did a retrospective analysis of a full-night recording to see if they could track dynamics over time and find some potential for that.

Lu: That part is where the ambition really comes through, because they looked at using a previously proposed convolutional bidirectional gated recurrent network model to profile inhalation, exhalation, and continuous adventitious sound episodes from those full-night recordings <ref:2408.16030#pg16>.

Jane: And what did that full-night analysis suggest? They found that the model predicted the snoring sounds mainly originated at the Velum level, followed by Oropharynx, with a slight rise in Epiglottis association in the last half hour of sleep <ref:2408.16030#pg18>.

Tom: That’s a shift from just classifying individual sound clips to understanding the pattern over time during sleep. It suggests that even if we miss some specific sites like T or E, we can still get a general idea of where the trouble is occurring across the night.

Meng: If we were actually putting this into a product, I’d be worried about how much data you need to make those full-night predictions robust enough to deploy reliably on real patient traffic. The paper suggests that longitudinal tracking *could* work, but scaling that prediction reliably is the next big hurdle <ref:2408.16030#pg18>.

Lu: I think the core idea is moving from just static classification to dynamic profiling, which is a much richer way to look at airway mechanics. The model's ability to profile the occupation rate of snoring by feeding segments into that VOTE model shows promise for tracking dynamics <ref:2408.16030#pg16>.

Jane: So, if you’re listening and you’re thinking about what this means for sleep apnea patients, it points toward a tool that can monitor things continuously rather than just taking snapshots of a sound clip.

Tom: And the title itself, "Deep learning-based multi-level airway collapse site localization on obstructive sleep apnea patients based on snoring sounds," tells you exactly what this research is trying to achieve with these models and these specific patient data.

Lu: It's about using deep learning to localize those collapses across multiple levels—VOTE and RP/RG—using the acoustic information from snoring sounds.

Jane: So, to wrap up, the study shows that while they have a strong model called AST beating ResNet-fifty for identifying obstructions at the Velum and Oropharynx levels, they still have significant challenges classifying Tongue Base, Epiglottis, and RP or RG regions due to limited data <ref:2408.16030#pg3>.

Meng: The implication for me is that if we want to build something deployable today, we need more variety in the training data for those harder-to-classify sites before we can claim this technology works universally <ref:2408.16030#pg3>.

Tom: It’s a promising non-invasive tool for tracking overnight airway dynamics and assisting in clinical treatment planning, as the paper concludes.

Lu: That sounds like it could help clinicians adjust time slices to fit regions of interest and synchronize those findings with things like PSG data <ref:2408.16030#pg18>.

Jane: So, this paper gives us a concrete direction for developing AI tools that can move beyond simple detection to actually mapping out the complexity of an airway collapse over time.

Conclusion: Tom: So we’ve looked at how AST outperformed ResNet-fifty on those V and O level identifications, and now we’re getting to the conclusion of this paper, "Deep learning-based multi-level airway collapse site localization on obstructive sleep apnea patients based on snoring sounds."

Jane: That title is pretty descriptive, isn't it? It tells you exactly what they did: use deep learning to find where the airway collapses are happening in people with sleep apnea by analyzing snoring sounds.

Lu: The authors used thirty-seven subjects from drug-induced sleep endoscopy data to train their models for these multi-level tasks across the VOTE levels and the RP or RG regions.

Meng: What’s important here is that they didn't just look at one thing, they tried to map out four different areas of the airway obstruction simultaneously.

Tom: And what they found is that while AST was better than ResNet-fifty for those top two levels—Velum and Oropharynx—it still hit a wall with the Tongue Base, Epiglottis, and Retroglossal regions because of how little data they had for those specific spots.

Jane: So, what does that mean in plain terms? It means the AI is pretty good at spotting trouble high up in the airway structure based on snoring sounds right now.

Lu: Exactly. The authors are showing that this acoustic profiling method has potential because it can track dynamics over a full night recording, which is a big step for understanding how sleep apnea progresses.

Tom: Tracking dynamics means you move beyond just saying "there's an obstruction" to figuring out *when* and *where* the obstruction shifts during sleep.

Jane: That’s the part where it gets interesting for clinical use. It suggests that these models could become a non-invasive way for doctors to track airway health overnight and help plan treatments before a surgery or procedure.

Meng: From my side, the challenge is getting that system to run reliably on real patient data without breaking at scale, especially since the authors mentioned those lower levels are still weak spots.

Lu: That's the next big question for future work. Can we expand that training set with more diverse snoring sounds so these models can get better at identifying those harder-to-spot areas?