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Deep Learning for Automated Analysis of Equine Radiographs in Orthopaedic Disorders

Retrospektive Studie der Pferde mit chronisch rekurrierender Blinddarmobstipation nach chirurgischer Versorgung mittels Erweiterungsplastik des Ostium caecocolicum

Liu Yang

Abstract

Orthopaedic disorders are one of the most common health problems in horses, which usually cause a reduction in performance, chronic pain, and chronic disability. These diseases require prompt and proper diagnosis to be cured and recovered. Radiography remains among the most commonly employed diagnostic tools for veterinarians to determine musculoskeletal diseases in horses, including fractures, joint degeneration, osteoarthritis, and bone lesions. The interpretation of radiographs, however, is quite familiar, and the quality of the diagnostic output is dictated by the veterinarian's expertise and the quality of the picture. AI development has demonstrated promising opportunities in recent years (especially deep learning), which can certainly automate and improve medical image analysis, including veterinary radiography. The study examines the application of deep learning algorithms to the automatic detection and classification of orthopaedic disorders in radiographic images of horses. The findings indicate that the deep learning algorithm is highly precise in detecting typical orthopaedic anomalies in equine radiographs. The automated system not only saves time spent interpreting images but also assists vets in making decisions in fields where radiology skills are usually weak. In addition, integrating deep learning into the veterinary diagnostic process can enhance early diagnosis of orthopaedic diseases, improve clinical decision-making, and support horse care. The suggested practice not only enhances animal welfare but also improves efficiency in veterinary practices, as radiographic investigations can be processed promptly and consistently. On balance, this paper highlights the significance of using artificial intelligence in veterinary imaging. It demonstrates how deep learning could be a useful tool for automating the study of equine orthopaedic conditions.

Keywords: Deep learning (DL), Equine Radiographs (ER), Orthopaedic Disorders (OD)

Pengliang Li, Xuyan Wu, Wei Wang, Cheng Zhang, Xiaochen Yang, Jixiang Li

Abstract

Impaction is one of the most common disease of the cecum in horses. It is important to distinguish between acute and chronic recurrent cases. Acute impaction occurs suddenly and resolves within a few days with medical and/or surgical treatment whereas in chronic cases the impaction within the base or the whole cecum occurs repeatedly at irregular intervals. The chronic recurrent cecal impaction (CRCI) is characterized by hypertrophy of muscle in the cecal base or entire cecum. The pathogenesis is not fully understood: hypothesis is that the cecal impaction induces a distension during the contraction of the circular muscle layer which is a stimulus of a hypertrophy of the circular and longitudinal muscle layer in the cecum. Furthermore, neuronal density in the plexus myentericus was found to be decreased significantly in the cecal wall of CRCI. Initially, in cases of CRCI the cecocolic orifice is not entirely blocked allowing partial transit of ingesta. Hypertrophy of the muscle layer (longitudinal and circular muscle) begins at the cecal base and as the disease progresses, the cecocolic opening becomes completely obstructed due to the automatic closure mechanism. The rectal and ultrasound exam are the most useful diagnostic steps while different degrees of cecal impaction and/or tympany, a marked thickening and cecal wall contractility due to palpation are found. The hypertrophy can be regarded as a pathognomonic sign. Treatment in our study was done by surgical enlargement of the cecocolic orifice (created by Huskamp 1990). Tissue samples of defined cecal regions were taken during surgery or necropsy: the circular and longitudinal muscle layer were significantly thicker, linear neuron densities were significantly lower each compared to clinically healthy horses. Based on smooth muscle thickening and neuron deficit, rectally palpable and ultrasound visible cecal wall thickening, horses suffering from CRI have a poor prognosis. Surgery by enlargement of the cecocolic orifice in horses with CRCI have a better prognosis if only the cecal base has a thickening and a normal cecal body wall.

Keywords: horses, chronic recurrent cecal impaction, surgical treatment enlargement cecocolic orifice, long-term survival

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Deep Learning for Automated Analysis of Equine Radiographs in Orthopaedic Disorders

Retrospektive Studie der Pferde mit chronisch rekurrierender Blinddarmobstipation nach chirurgischer Versorgung mittels Erweiterungsplastik des Ostium caecocolicum

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