A Machine Learning-Based Screening Test for Sarcopenic Dysphagia Using Image Recognition

Sakai, Kotomi; Gilmour, Stuart; Hoshino, Eri; Nakayama, Enri; Momosaki, Ryo; Sakata, Nobuchika; Yoneoka, Daisuke

doi:10.3390/nu13114009

Cited by 6 publications

(8 citation statements)

References 35 publications

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“…Therefore, the final explanation provided by the system is effective and acceptable. This goes beyond most existing approaches for this task (except the VFSS approach [ 25 ]), because they only provide predictions or classifications without providing proper interpretable information for the diagnostician [ 28 , 35 , 37 , 42 , 43 , 62 , 63 ]. As this lack of transparency conflicts with EU GDPR, which prohibits decisions based solely on automated processing [ 44 , 45 ], a subsequent FEES or VFSS would become necessary, in any event, before critical decisions—such as abstinence from food, insertion of a nasogastric tube, or even re-intubation and tracheotomy—could be made.…”

Section: Discussionmentioning

confidence: 99%

“…Studies looking at aspiration detection using image data (VFSS) [ 25 ] or swallow-onset detection [ 41 ] also yield promising results. A different approach is an image analysis of the external neck appearance for the detection of sarcopenic dysphagia [ 42 ]. Finally, speech recordings have also been investigated for the presence of dysphagia [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

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AI-Based Detection of Aspiration for Video-Endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome

Konradi

Zajber

Betz

et al. 2022

Sensors

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Disorders of swallowing often lead to pneumonia when material enters the airways (aspiration). Flexible Endoscopic Evaluation of Swallowing (FEES) plays a key role in the diagnostics of aspiration but is prone to human errors. An AI-based tool could facilitate this process. Recent non-endoscopic/non-radiologic attempts to detect aspiration using machine-learning approaches have led to unsatisfying accuracy and show black-box characteristics. Hence, for clinical users it is difficult to trust in these model decisions. Our aim is to introduce an explainable artificial intelligence (XAI) approach to detect aspiration in FEES. Our approach is to teach the AI about the relevant anatomical structures, such as the vocal cords and the glottis, based on 92 annotated FEES videos. Simultaneously, it is trained to detect boluses that pass the glottis and become aspirated. During testing, the AI successfully recognized the glottis and the vocal cords but could not yet achieve satisfying aspiration detection quality. While detection performance must be optimized, our architecture results in a final model that explains its assessment by locating meaningful frames with relevant aspiration events and by highlighting suspected boluses. In contrast to comparable AI tools, our framework is verifiable and interpretable and, therefore, accountable for clinical users.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AI-Based Detection of Aspiration for Video-Endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome

Konradi

Zajber

Betz

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Therefore, the final explanation provided by the system is effective and acceptable. This goes beyond most existing approaches for this task (except the VFSS approach [25]), since they only provide predictions or classifications without providing proper interpretable information for the diagnostician [28,35,37,42,43,62,63]. Since this lack of transparency conflicts with EU GDPR, as it prohibits decision solely based on automated processing [44,64], a subsequent FEES or VFSS would become necessary anyway before critical decisions like abstinence from food, insertion of a nasogastric tube, or even re-intubation and tracheotomy could be made.…”

Section: Discussionmentioning

confidence: 99%

“…Studies looking at aspiration detection using image data (VFSS) [25] or swallow onset detection [41] also yield promising results. A different approach is an image analysis of the external neck appearance for the detection of sarcopenic dysphagia [42]. Finally, also speech recordings have been investigated for the presence of dysphagia [43].…”

Section: Introductionmentioning

confidence: 99%

AI-based Detection of Aspiration for Video-endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome

Konradi¹,

Zajber²,

Betz³

et al. 2022

Preprint

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Disorders of swallowing often lead to pneumonia when material enters the airways (aspiration). Flexible Endoscopic Evaluation of Swallowing (FEES) plays a key role in the diagnostics of aspiration but is prone to human errors. An AI-based tool could facilitate this process. Recent non-endoscopic/non-radiologic attempts to detect aspiration using machine-learning approaches have led to unsatisfying accuracy and show black box characteristics. Hence, for clinical users it is hard to trust in these model decisions. Our aim is to introduce an explainable artificial intelligence (XAI) approach to detect aspiration in FEES. Our approach is to teach the AI about the relevant anatomical structures like the vocal cords and the glottis based on 92 annotated FEES videos. Simultaneously, it is trained to detect bolus that passes the glottis and becomes aspirated. During testing, the AI successfully recognized glottis and vocal cords, but could not yet achieve satisfying aspiration detection quality. Albeit detection performance has to be optimized, our architecture results in a final model that explains its assessment by locating meaningful frames with relevant aspiration events and by highlighting the suspected bolus. In contrast to comparable AI tools, our framework is verifiable, interpretable and therefor accountable for clinical users.

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“…The representative DL technique, namely convolutional neural networks (CNNs) exhibit remarkable capability in feature extraction and thus has been widely employed for image classification ( 3 – 5 ). Over the past few years, a variety of DL-based medical applications have been reported, such as the assisted diagnosis of digestive tract tumors with pathology, MRI, and ultrasound images ( 6 – 9 ), the diagnosis of dysphagia with photographs of the anterior neck ( 10 ), and the precise location and identification of cells in microscopic images ( 11 ). In the dermatology field, DL uses trunk or longitudinal skin stratification images of patients' lesions to diagnose skin tumors, atopic dermatitis, psoriasis, and fungal infections ( 12 – 15 ).…”

Section: Introductionmentioning

confidence: 99%

A deep learning-based approach toward differentiating scalp psoriasis and seborrheic dermatitis from dermoscopic images

Zhang

Shen²,

Jianwen³

et al. 2022

Front. Med.

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ObjectivesThis study aims to develop a new diagnostic method for discriminating scalp psoriasis and seborrheic dermatitis based on a deep learning (DL) model, which uses the dermatoscopic image as input and achieved higher accuracy than dermatologists trained with dermoscopy.MethodsA total of 1,358 pictures (obtained from 617 patients) with pathological and diagnostic confirmed skin diseases (508 psoriases, 850 seborrheic dermatitides) were randomly allocated into the training, validation, and testing datasets (1,088/134/136) in this study. A DL model concerning dermatoscopic images was established using the transfer learning technique and trained for diagnosing two diseases.ResultsThe developed DL model exhibits good sensitivity, specificity, and Area Under Curve (AUC) (96.1, 88.2, and 0.922%, respectively), it outperformed all dermatologists in the diagnosis of scalp psoriasis and seborrheic dermatitis when compared to five dermatologists with various levels of experience. Furthermore, non-proficient doctors with the assistance of the DL model can achieve comparable diagnostic performance to dermatologists proficient in dermoscopy. One dermatology graduate student and two general practitioners significantly improved their diagnostic performance, where their AUC values increased from 0.600, 0.537, and 0.575 to 0.849, 0.778, and 0.788, respectively, and their diagnosis consistency was also improved as the kappa values went from 0.191, 0.071, and 0.143 to 0.679, 0.550, and 0.568, respectively. DL enjoys favorable computational efficiency and requires few computational resources, making it easy to deploy in hospitals.ConclusionsThe developed DL model has favorable performance in discriminating two skin diseases and can improve the diagnosis, clinical decision-making, and treatment of dermatologists in primary hospitals.

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A Machine Learning-Based Screening Test for Sarcopenic Dysphagia Using Image Recognition

Cited by 6 publications

References 35 publications

AI-Based Detection of Aspiration for Video-Endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome

AI-Based Detection of Aspiration for Video-Endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome

AI-based Detection of Aspiration for Video-endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome

A deep learning-based approach toward differentiating scalp psoriasis and seborrheic dermatitis from dermoscopic images

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