Segment-Based Spotting of Bowel Sounds Using Pretrained Models in Continuous Data Streams

Baronetto, Annalisa; Graf, Luisa S.; Fischer, Sarah; Neurath, Markus F.; Amft, Oliver

doi:10.1109/jbhi.2023.3269910

Cited by 4 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our model outperformed the CNN by Wang et al [ 9 ] not only in temporal spotting resolution (25 ms of EffUNet vs 60 ms of the CNN), but also in spotting performance on highly imbalanced data (80% median precision of EffUNet vs 5% median precision of the CNN). Using EfficientNet for our model encoder allowed us to leverage pretraining to increase model robustness against noise, as shown in [ 14 ]. Further work may investigate whether less complex models than EffUNet could be optimized for natural, imbalanced data.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies (eg, [ 14 ]) have attempted to reduce the amount of audio data to be manually analyzed with segment-based approaches that detected audio sections containing BS events. Moreover, methods were proposed to improve BS event detection and ease expert examination, by determining the onset and offset of the BS patterns in audio data streams (eg, [ 9 , 15 ]).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiscale Bowel Sound Event Spotting in Highly Imbalanced Wearable Monitoring Data: Algorithm Development and Validation Study

Baronetto,

Graf,

Fischer

et al. 2024

JMIR AI

Self Cite

View full text Add to dashboard Cite

Background Abdominal auscultation (i.e., listening to bowel sounds (BSs)) can be used to analyze digestion. An automated retrieval of BS would be beneficial to assess gastrointestinal disorders noninvasively. Objective This study aims to develop a multiscale spotting model to detect BSs in continuous audio data from a wearable monitoring system. Methods We designed a spotting model based on the Efficient-U-Net (EffUNet) architecture to analyze 10-second audio segments at a time and spot BSs with a temporal resolution of 25 ms. Evaluation data were collected across different digestive phases from 18 healthy participants and 9 patients with inflammatory bowel disease (IBD). Audio data were recorded in a daytime setting with a smart T-Shirt that embeds digital microphones. The data set was annotated by independent raters with substantial agreement (Cohen κ between 0.70 and 0.75), resulting in 136 hours of labeled data. In total, 11,482 BSs were analyzed, with a BS duration ranging between 18 ms and 6.3 seconds. The share of BSs in the data set (BS ratio) was 0.0089. We analyzed the performance depending on noise level, BS duration, and BS event rate. We also report spotting timing errors. Results Leave-one-participant-out cross-validation of BS event spotting yielded a median F1-score of 0.73 for both healthy volunteers and patients with IBD. EffUNet detected BSs under different noise conditions with 0.73 recall and 0.72 precision. In particular, for a signal-to-noise ratio over 4 dB, more than 83% of BSs were recognized, with precision of 0.77 or more. EffUNet recall dropped below 0.60 for BS duration of 1.5 seconds or less. At a BS ratio greater than 0.05, the precision of our model was over 0.83. For both healthy participants and patients with IBD, insertion and deletion timing errors were the largest, with a total of 15.54 minutes of insertion errors and 13.08 minutes of deletion errors over the total audio data set. On our data set, EffUNet outperformed existing BS spotting models that provide similar temporal resolution. Conclusions The EffUNet spotter is robust against background noise and can retrieve BSs with varying duration. EffUNet outperforms previous BS detection approaches in unmodified audio data, containing highly sparse BS events.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiscale Bowel Sound Event Spotting in Highly Imbalanced Wearable Monitoring Data: Algorithm Development and Validation Study

Baronetto,

Graf,

Fischer

et al. 2024

JMIR AI

Self Cite

View full text Add to dashboard Cite

show abstract

A Resource-Efficient Deep Learning Approach to Visual-Based Cattle Geographic Origin Prediction

Ray,

Bakshi,

Kumar Sa

et al. 2024

Mobile Netw Appl

View full text Add to dashboard Cite

The sound of surgery-development of an acoustic trocar system enabling laparoscopic sound analysis

Ostler-Mildner,

Wegener,

Fuchtmann

et al. 2024

Int J CARS

View full text Add to dashboard Cite

Purpose Acoustic information can contain viable information in medicine and specifically in surgery. While laparoscopy depends mainly on visual information, our goal is to develop the means to capture and process acoustic information during laparoscopic surgery. Methods To achieve this, we iteratively developed three prototypes that will overcome the abdominal wall as a sound barrier and can be used with standard trocars. We evaluated them in terms of clinical applicability and sound transmission quality. Furthermore, the applicability of each prototype for sound classification based on machine learning was evaluated. Results Our developed prototypes for recording airborne sound from the intraperitoneal cavity represent a promising solution suitable for real-world clinical usage All three prototypes fulfill our set requirements in terms of clinical applicability (i.e., air-tightness, invasiveness, sterility) and show promising results regarding their acoustic characteristics and the associated results on ML-based sound classification. Conclusion In summary, our prototypes for capturing acoustic information during laparoscopic surgeries integrate seamlessly with existing procedures and have the potential to augment the surgeon’s perception. This advancement could change how surgeons interact with and understand the surgical field.

show abstract

Segment-Based Spotting of Bowel Sounds Using Pretrained Models in Continuous Data Streams

Cited by 4 publications

References 32 publications

Multiscale Bowel Sound Event Spotting in Highly Imbalanced Wearable Monitoring Data: Algorithm Development and Validation Study

Multiscale Bowel Sound Event Spotting in Highly Imbalanced Wearable Monitoring Data: Algorithm Development and Validation Study

A Resource-Efficient Deep Learning Approach to Visual-Based Cattle Geographic Origin Prediction

The sound of surgery-development of an acoustic trocar system enabling laparoscopic sound analysis

Contact Info

Product

Resources

About