Use of artificial intelligence for the diagnosis of cholesteatoma

Tseng, Christopher C.; Lim, Valerie; Jyung, Robert W.

doi:10.1002/lio2.1008

Cited by 7 publications

(5 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the GPT-4V diagnostic accuracy for chole was higher than that of pediatricians, indicating that GPT-4V could help nonotolaryngologists diagnose chole. In a previous report, a dedicated AI model had a diagnostic accuracy of approximately 90% for chole [ 25 ]; therefore, the combination of such a system and GPT-4V would be useful to improve the accuracy of chole detection.…”

Section: Discussionmentioning

confidence: 99%

Feasibility of Multimodal Artificial Intelligence Using GPT-4 Vision for the Classification of Middle Ear Disease: Qualitative Study and Validation

Noda,

Yoshimura,

Okubo

et al. 2024

JMIR AI

View full text Add to dashboard Cite

Background The integration of artificial intelligence (AI), particularly deep learning models, has transformed the landscape of medical technology, especially in the field of diagnosis using imaging and physiological data. In otolaryngology, AI has shown promise in image classification for middle ear diseases. However, existing models often lack patient-specific data and clinical context, limiting their universal applicability. The emergence of GPT-4 Vision (GPT-4V) has enabled a multimodal diagnostic approach, integrating language processing with image analysis. Objective In this study, we investigated the effectiveness of GPT-4V in diagnosing middle ear diseases by integrating patient-specific data with otoscopic images of the tympanic membrane. Methods The design of this study was divided into two phases: (1) establishing a model with appropriate prompts and (2) validating the ability of the optimal prompt model to classify images. In total, 305 otoscopic images of 4 middle ear diseases (acute otitis media, middle ear cholesteatoma, chronic otitis media, and otitis media with effusion) were obtained from patients who visited Shinshu University or Jichi Medical University between April 2010 and December 2023. The optimized GPT-4V settings were established using prompts and patients’ data, and the model created with the optimal prompt was used to verify the diagnostic accuracy of GPT-4V on 190 images. To compare the diagnostic accuracy of GPT-4V with that of physicians, 30 clinicians completed a web-based questionnaire consisting of 190 images. Results The multimodal AI approach achieved an accuracy of 82.1%, which is superior to that of certified pediatricians at 70.6%, but trailing behind that of otolaryngologists at more than 95%. The model’s disease-specific accuracy rates were 89.2% for acute otitis media, 76.5% for chronic otitis media, 79.3% for middle ear cholesteatoma, and 85.7% for otitis media with effusion, which highlights the need for disease-specific optimization. Comparisons with physicians revealed promising results, suggesting the potential of GPT-4V to augment clinical decision-making. Conclusions Despite its advantages, challenges such as data privacy and ethical considerations must be addressed. Overall, this study underscores the potential of multimodal AI for enhancing diagnostic accuracy and improving patient care in otolaryngology. Further research is warranted to optimize and validate this approach in diverse clinical settings.

show abstract

Section: Discussionmentioning

confidence: 99%

Feasibility of Multimodal Artificial Intelligence Using GPT-4 Vision for the Classification of Middle Ear Disease: Qualitative Study and Validation

Noda,

Yoshimura,

Okubo

et al. 2024

JMIR AI

View full text Add to dashboard Cite

show abstract

“…A few AI models have recently been developed to classify common middle ear conditions, such as CSOM, otitis media with effusion, and cholesteatoma [38][39][40][41]. However, these models were primarily based on traditional otoscopic images, which are potentially limited by a narrow field of view and insufficient diagnostic information.…”

Section: Comparison With Prior Workmentioning

confidence: 99%

A 3D and Explainable Artificial Intelligence Model for Evaluation of Chronic Otitis Media Based on Temporal Bone Computed Tomography: Model Development, Validation, and Clinical Application

Chen,

Li,

Sun

et al. 2024

J Med Internet Res

View full text Add to dashboard Cite

Background: Computed tomography (CT) of the temporal bone has become a critical diagnostic approach to chronic otitis media (COM), but it requires training and experience for interpretation. Artificial intelligence may assist clinicians in evaluating COM using CT with efficiency and reliability, but the logic for decision-making can be incomprehensible and there is currently no model that makes full use of the multidimensional diagnostic information.Objective: This study was to develop an explainable and three-dimensional (3D) deep learning framework for detection and differential diagnosis of COM based on CT.Methods: Temporal bone CT scans were retrospectively obtained from patients receiving surgeries for COM between December 2015 and July 2021 at two independent institutes. The region of interest containing the middle ear was automatically segmented, followed by 3D convolutional neural networks trained to identify pathological ears and cholesteatoma. Gradient-weighted class activation mapping was used to generate heatmaps highlighting the critical regions for decision-making. Model performance was evaluated over five rounds of cross-validation and external validation and benchmarked against clinical experts. Results:The internal and the external datasets contained 1,661 patients (number of eligible ears[n]= 3,153) and 108 patients (n=211), respectively. The deep learning model achieved decent and comparable area under the receiver operating characteristic curve (AUROC) scores ([mean ± SD]: 0.96±0.01 and 0.93±0.01) and accuracies (87.8±1.7% and 84.3±1.5%) in detection of pathological ears on two datasets. Similar outcomes were also observed in identifying cholesteatoma, with AUROC of 0.85±0.03 and 0.83±0.05, and accuracies of 78.3±4.0% and 81.3±3.3%, respectively. The model exhibited equivalent or superior performance and a much higher consistency compared to experts' averages in both tasks. The heatmaps properly highlighted the middle ear and mastoid regions, consistent with human knowledge in interpreting temporal bone CT. Conclusions:This study suggests the feasibility of a 3D deep learning framework in automatic evaluation of COM using CT. This model demonstrates decent performance, generalizability, and transparency, making it a useful tool to assist clinicians in assessment of COM.

show abstract

“…AI’s role in the management of chronic otitis media (COM) has been well-established, including image analysis, automated diagnosis, surgical planning, treatment recommendations, monitoring, and prognostication [ 43 , 44 ]. Multiple software programs, including CNN, VGG-16, and MobileNetV2, have been used for the detection of COM [ 45 - 58 ]. Studies involving AI technologies in middle ear diseases are presented in Table 2 .…”

Section: Reviewmentioning

confidence: 99%

“…TB: temporal bone; Net: network; CNN: convolutional neural networks; DC: Dice coefficient; ASSD: average symmetric surface distance; AH-Net: anisotropic hybrid network; ResNet: residual neural network; DSC: Dice similarity coefficient; PWD: patch-wise densely connected; YOLACT: You Only Look At CoefficienTs; DSD: deep supervised densely; MSSIM: mean structural similarity index; IoU: intersection over union; PSO: particle swarm optimization; BF: Bayes factors; N/S: not specifiedAI in middle ear diseaseChronic Otitis Media With or Without CholesteatomaAI's role in the management of chronic otitis media (COM) has been well-established, including image analysis, automated diagnosis, surgical planning, treatment recommendations, monitoring, and prognostication[43,44]. Multiple software programs, including CNN, VGG-16, and MobileNetV2, have been used for the detection of COM[45][46][47][48][49][50][51][52][53][54][55][56][57][58]. Studies involving AI technologies in middle ear diseases are presented in Table2.…”

mentioning

confidence: 99%

Applications of Artificial Intelligence in Temporal Bone Imaging: Advances and Future Challenges

Petsiou,

Martinos,

Spinos

2023

Cureus

View full text Add to dashboard Cite

The applications of artificial intelligence (AI) in temporal bone (TB) imaging have gained significant attention in recent years, revolutionizing the field of otolaryngology and radiology. Accurate interpretation of imaging features of TB conditions plays a crucial role in diagnosing and treating a range of ear-related pathologies, including middle and inner ear diseases, otosclerosis, and vestibular schwannomas. According to multiple clinical studies published in the literature, AI-powered algorithms have demonstrated exceptional proficiency in interpreting imaging findings, not only saving time for physicians but also enhancing diagnostic accuracy by reducing human error. Although several challenges remain in routinely relying on AI applications, the collaboration between AI and healthcare professionals holds the key to better patient outcomes and significantly improved patient care. This overview delivers a comprehensive update on the advances of AI in the field of TB imaging, summarizes recent evidence provided by clinical studies, and discusses future insights and challenges in the widespread integration of AI in clinical practice.

show abstract

Use of artificial intelligence for the diagnosis of cholesteatoma

Cited by 7 publications

References 37 publications

Feasibility of Multimodal Artificial Intelligence Using GPT-4 Vision for the Classification of Middle Ear Disease: Qualitative Study and Validation

Feasibility of Multimodal Artificial Intelligence Using GPT-4 Vision for the Classification of Middle Ear Disease: Qualitative Study and Validation

A 3D and Explainable Artificial Intelligence Model for Evaluation of Chronic Otitis Media Based on Temporal Bone Computed Tomography: Model Development, Validation, and Clinical Application

Applications of Artificial Intelligence in Temporal Bone Imaging: Advances and Future Challenges

Contact Info

Product

Resources

About