Development of a Comprehensive Lesion Severity Classification Model for Largemouth Bass (Micropterus salmoides) Ranavirus (LMBV) Based on Machine Vision

This study presents the development of a quantitative evaluation method utilizing machine vision technology to characterize the extent of body surface damage in largemouth bass (<i>Micropterus salmoides</i>) infected with largemouth bass ranavirus (LMBV). High-resolution, multi-angle images (6000 × 4000 pixels) of the body surface from 239 infected specimens were acquired at a fixed distance of 40 cm using a SONY ILCE-7RM3 digital camera within a GODOX-LST60 softbox. Key parameters, including the number of segmented injury areas, the count of body surface lesions, and the total lesion area, were analyzed. These parameters were integrated through principal component analysis (PCA) to construct a comprehensive damage scoring model. The severity of viral-induced body surface damage was categorized into four grades: uninjured (0), minor injury (1), moderate injury (2), and severe injury (3). Histopathological examination revealed that early-stage infection (grade 1) predominantly exhibited localized hemorrhagic spots in the muscular region of the body side (B/E region) with limited lesion area. In contrast, moderate to severe infections (grades 2-3) were characterized by extensive ulceration, muscle necrosis, and visceral lesions, including hepatic fibrosis and splenic granulomatous formations. Quantitative real-time PCR (qRT-PCR) analysis demonstrated a progressive upregulation of pro-inflammatory cytokines (<i>IL-6</i>, <i>IL-8</i>, <i>TNF-α</i>, <i>CXCL2</i>) in immune organs, concomitant with increased expression of apoptosis-related genes (<i>CASP8</i>, <i>CYC</i>). This study successfully established a rapid and objective quantitative grading system for ranavirus infection, offering a novel technical approach for early diagnosis and precise prevention and control strategies against largemouth bass ranavirus.