Detection of canine external ear canal lesions using artificial intelligence

BACKGROUND: Early and accurate diagnosis of otitis externa is crucial for correct management yet can often be challenging. Artificial intelligence (AI) is a valuable diagnostic tool in human medicine. Currently, no such tool is available in veterinary dermatology/otology. OBJECTIVES: As a proof-of-concept, we developed and evaluated a novel YOLOv5 object detection model for identifying healthy ear canals, otitis or masses in the canine ear canal. ANIMALS: Digital images of ear canals from dogs with healthy ears, otitis and masses in the ear canal were used. MATERIALS AND METHODS: Four variants of the YOLOv5 model were trained, each using a different training dataset. The prediction performance metrics used to evaluate them include F1/confidence-curves, mean average precision (mAP50), precision (P), recall (R) and average precision (AP) for accuracy. These are quantifiable performance metrics used to evaluate the efficacy of each variant. RESULTS: All four variants were capable of detecting and classifying the ear canal. However, training datasets with many duplicates (A and C) inflated performance metrics as a consequence of data leakage, potentially compromising their effectiveness on unseen images. Additionally, larger datasets (without duplicates) demonstrated superior performance metrics compared to model variants trained on smaller datasets (without duplicates). CONCLUSIONS AND CLINICAL RELEVANCE: This novel AI object detection model has the potential for application in the field of veterinary dermatology. An external validation study is needed prior to clinical deployment.

Authors: N. Apostolopoulos, S. Murray, S. Aravamuthan and D. Döpfer Title: Detection of canine external ear canal lesions using artificial intelligence Full source: Vet Dermatol, 2025,Vol Document type: Journal Article
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Abstract	Source
BACKGROUND: Early and accurate diagnosis of otitis externa is crucial for correct management yet can often be challenging. Artificial intelligence (AI) is a valuable diagnostic tool in human medicine. Currently, no such tool is available in veterinary dermatology/otology. OBJECTIVES: As a proof-of-concept, we developed and evaluated a novel YOLOv5 object detection model for identifying healthy ear canals, otitis or masses in the canine ear canal. ANIMALS: Digital images of ear canals from dogs with healthy ears, otitis and masses in the ear canal were used. MATERIALS AND METHODS: Four variants of the YOLOv5 model were trained, each using a different training dataset. The prediction performance metrics used to evaluate them include F1/confidence-curves, mean average precision (mAP50), precision (P), recall (R) and average precision (AP) for accuracy. These are quantifiable performance metrics used to evaluate the efficacy of each variant. RESULTS: All four variants were capable of detecting and classifying the ear canal. However, training datasets with many duplicates (A and C) inflated performance metrics as a consequence of data leakage, potentially compromising their effectiveness on unseen images. Additionally, larger datasets (without duplicates) demonstrated superior performance metrics compared to model variants trained on smaller datasets (without duplicates). CONCLUSIONS AND CLINICAL RELEVANCE: This novel AI object detection model has the potential for application in the field of veterinary dermatology. An external validation study is needed prior to clinical deployment.