Point-of-care platform integrated with deep-learning, convolutional neural network algorithms effectively evaluates canine and feline peripheral blood smears.

IF 1.3 3区农林科学 Q2 VETERINARY SCIENCES American journal of veterinary research Pub Date : 2024-12-20 DOI:10.2460/ajvr.24.08.0226

Eric Morissette, Cory D Penn, Ruth A Hall Sedlak, Austin J Rhodes, Dan S Tippetts, Mike Loenser, Richard Goldstein

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Abstract

Objective: To perform a diagnostic assessment of a point-of-care veterinary multiuse platform integrated with a model comprised of deep-learning, convolutional neural network algorithms for evaluating canine/feline peripheral blood smears compared to board-certified clinical pathologists (CPs).

Methods: This study had a blinded, randomized, incomplete block design, and results were compared between CPs and algorithms. Blood smears from convenience samples from veterinary diagnostic reference laboratories from October to December 2021 were used. Study phase A comprised 2 parts: (1) object class identifier algorithm (leukocytes, platelets, polychromatophils, and nucleated erythrocytes) versus CP within the same field of view (FOV); and (2) monolayer detection algorithm plus object class identifier algorithm versus CPs with different FOVs. Study phase B comprised algorithms versus CP for platelet clump identification. Study phase C comprised algorithms versus CP for polychromatophil identification. Metrics including sensitivity, specificity, and agreement were used.

Results: The sample size was 59 dogs and 60 cats in phase A, 92 dogs and 69 cats in phase B, and 47 dogs and 12 cats in phase C. For study phase A, part 1, the 5-part leukocyte differential count agreement was 96.6% for canine and 91.7% for feline blood smears, and for part 2, the agreement for estimated total leukocyte, platelet, polychromatophil, and nucleated erythrocyte counts ranged from 70% to 95% across species. In study phase B, the algorithm had 90% sensitivity and 88% specificity. The algorithm for polychromatophils had 100% agreement with CP results in phase C.

Conclusions: This platform achieved results comparable to those of CPs. Results are meant to complement automated CBC results.

Clinical relevance: Veterinarians may add this assessment as part of their standard in-clinic hematology analysis for patients.

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与深度学习、卷积神经网络算法集成的即时护理平台有效地评估犬和猫的外周血涂片。

目的：与委员会认证的临床病理学家（CPs）相比，对与深度学习、卷积神经网络算法模型集成的点护理兽医多功能平台进行诊断评估，用于评估犬/猫外周血涂片。方法：本研究采用盲法、随机、不完全区组设计，将CPs和算法的结果进行比较。使用了2021年10月至12月兽医诊断参考实验室便利样本的血液涂片。研究A阶段包括两个部分：(1)目标类识别算法（白细胞、血小板、嗜多色细胞和有核红细胞）与相同视场（FOV）内的CP；(2)单层检测算法加对象类标识算法与不同视场的CPs的对比。研究B阶段包括血小板团识别的算法与CP。研究C阶段包括算法与CP的多染色质鉴定。使用的指标包括敏感性、特异性和一致性。结果：A期的样本量为59只狗和60只猫，B期为92只狗和69只猫，c期为47只狗和12只猫。在研究A期第一部分中，犬血涂片的5部分白细胞差异计数一致性为96.6%，猫血涂片一致性为91.7%，在第二部分中，估计白细胞、血小板、多染色质和有核红细胞总数的一致性在70%到95%之间。在研究B阶段，该算法的灵敏度为90%，特异性为88%。该算法与CP的c期结果吻合度为100%。结论：该平台的结果与CP相当。结果是为了补充自动CBC结果。临床相关性：兽医可以将此评估作为患者标准临床血液学分析的一部分。

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来源期刊

American journal of veterinary research 农林科学-兽医学

CiteScore

1.70

自引率

10.00%

发文量

186

审稿时长

3 months

期刊介绍： The American Journal of Veterinary Research supports the collaborative exchange of information between researchers and clinicians by publishing novel research findings that bridge the gulf between basic research and clinical practice or that help to translate laboratory research and preclinical studies to the development of clinical trials and clinical practice. The journal welcomes submission of high-quality original studies and review articles in a wide range of scientific fields, including anatomy, anesthesiology, animal welfare, behavior, epidemiology, genetics, heredity, infectious disease, molecular biology, oncology, pharmacology, pathogenic mechanisms, physiology, surgery, theriogenology, toxicology, and vaccinology. Species of interest include production animals, companion animals, equids, exotic animals, birds, reptiles, and wild and marine animals. Reports of laboratory animal studies and studies involving the use of animals as experimental models of human diseases are considered only when the study results are of demonstrable benefit to the species used in the research or to another species of veterinary interest. Other fields of interest or animals species are not necessarily excluded from consideration, but such reports must focus on novel research findings. Submitted papers must make an original and substantial contribution to the veterinary medicine knowledge base; preliminary studies are not appropriate.