US 12,406,368 B2
Visual analysis of sperm DNA fragmentation
Ifthakaar Shaik, Sandton (ZA); Pu-Ju Lin, Woodmead Ext. (CA); and Byron Alexander Jacobs, Johannesburg (CA)
Assigned to VitruvianMD PTE Ltd, Singapore (SG)
Filed by VitruvianMD PTE Ltd, Singapore (SG)
Filed on Mar. 19, 2025, as Appl. No. 19/084,122.
Application 19/084,122 is a continuation of application No. PCT/IB2023/059595, filed on Sep. 27, 2023.
Claims priority of application No. 2022/11329 (ZA), filed on Oct. 17, 2022.
Prior Publication US 2025/0238927 A1, Jul. 24, 2025
Int. Cl. G06T 7/00 (2017.01); G16B 40/00 (2019.01)
CPC G06T 7/0012 (2013.01) [G16B 40/00 (2019.02); G06T 2207/10016 (2013.01); G06T 2207/10056 (2013.01); G06T 2207/20016 (2013.01); G06T 2207/20036 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30204 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method for analysing DNA fragmentation in a sperm cell by approximating the output of a pre-selected chemical assay of sperm DNA fragmentation, the method comprising:
(i) providing an image of the sperm cell, under brightfield and/or phase contrast with a total magnification of 400× to 1000×;
(ii) evaluating the image of the sperm cell to identify and/or measure a pre-selected biomarker, comprising:
processing the image of the sperm cell using an image processing neural network to generate a network output that characterizes one or more morphological features of the sperm cell,
wherein the image processing neural network comprises a plurality of convolutional neural network layers, and
wherein the pre-selected biomarker is based on the morphological features of the sperm cell; and
(iii) approximating the output of the pre-selected chemical assay of sperm DNA fragmentation of the sperm cell by subjecting the identified and/or measured biomarker to a first machine learning analysis, comprising:
processing a model input comprising the identified and/or measured biomarker using an assay prediction machine learning model and in accordance with trained values of a set of machine learning model parameters to generate a prediction for the output of the pre-selected chemical assay of sperm DNA fragmentation of the sperm cell,
wherein the assay prediction machine learning model generates a model output that comprises a respective prediction for the output of each of a plurality of pre-selected chemical assays of sperm DNA fragmentation of the sperm cell.