New Criteria for Histopathological Classification of Testis Based on Johnsen Score for Male Infertility Using Automated Deep Learning Software

2020 ◽  
Author(s):  
Yurika Ito ◽  
Mami Unagami ◽  
Fumito Yamabe ◽  
Yozo Mistui ◽  
Koichi Nakajima ◽  
...  
2021 ◽  
Author(s):  
Yurika Ito ◽  
Mami Unagami ◽  
Fumito Yamabe ◽  
Yozo Mitsui ◽  
Koichi Nakajima ◽  
...  

Abstract We examined whether a tool for determining Johnsen scores automatically using artificial intelligence (AI) could be used in place of traditional Johnsen scoring to support pathologists’ evaluations. Average precision, precision, and recall assessed by the Google Cloud AutoML vision platform. We obtained testicular tissues for the 275 patients and were able to make 264 haematoxylin and eosin (H&E)-stained glass microscope slides. In addition, we cut out of parts of the histopathology images (5.0 X 5.0 cm) for expansion of Johnsen’s characteristic areas with seminiferous tubules. We defined four labels: Johnsen score 1-3, 4-5, 6-7, and 8-10 to distinguish Johnsen scores in clinical practice. All images were uploaded to the Google Cloud AutoML vision platform. We obtained a dataset of 7155 images at magnification X400 and a dataset of 9822 expansion images for the 5.0 X 5.0 cm cutouts. For the X400 magnification image dataset, the average precision (positive predictive value) of the algorithm was 82.6%, precision was 80.31%, and recall was 60.96%. For the expansion image dataset (5.0 X 5.0 cm), the average precision of the algorithm was 99.5%, precision was 96.29%, and recall was 96.23%. This is the first report of an AI-based algorithm for predicting Johnsen scores.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Osamu Iizuka ◽  
Fahdi Kanavati ◽  
Kei Kato ◽  
Michael Rambeau ◽  
Koji Arihiro ◽  
...  

Retina ◽  
2019 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Kanwal K. Bhatia ◽  
Mark S. Graham ◽  
Louise Terry ◽  
Ashley Wood ◽  
Paris Tranos ◽  
...  

2016 ◽  
pp. 59-65 ◽  
Author(s):  
Van Mao Nguyen

Background: Lymphoma is one of the most ten common cancers in the world as well as in Vietnam which has been ever increasing. It was divided into 2 main groups Hodgkin and non – Hodgkin lymphoma in which non-Hodgkin lymphoma appeared more frequency, worse prognosis and different therapy. Objectives: - To describe some common characteristics in patients with non – Hodgkin lymphoma; - To determine the proportion between Hodgkin and non- Hodgkin lymphoma, histopathological classification of classical Hodgkin by modified Rye 1966 and non-Hodgkin lymphoma by Working Formulation (WF) of US national oncology institute 1982. Materials and Method: This cross-sectional study was conducted on 65 patients with Hodgkin and non- Hodgkin lymphoma diagnosed definitely by histopathology at Hue Central Hospital and Hue University Hospital. Results:. The ratio of male/female for the non-Hodgkin lymphoma was 1.14/1, the most frequent range of age was 51-60 accounting for 35%, not common under 40 years. Non - Hodgkin lymphoma appeared at lymph node was the most common (51.7%), at the extranodal site was rather high 48.3%. The non - Hodgkin lymphoma proportion was predominant 92.3% comparing to the Hodgkin lymphoma only 7.7%; The most WF type was WF7 (53.3%), following the WF6 18,3% and WF5 11,7%; The intermediate malignancy grade of non- Hodgkin lymphoma was the highest proportion accouting for 85%, then the low and the high one 8.3% and 6.7% respectively. Conclusion: The histopathological classification and the malignant grade of lymphoma for Hodgkin and non - Hodgkin lymphoma played a practical role for the prognosis and the treatment orientation, also a fundamental one for the modern classification of non - Hodgkin lymphoma nowadays. Key words: lymphoma, Hodgkin lymphoma, non-Hodgkin lymphoma, classication, grade, histopathology, lymph node


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