# US 12,005,443 B2 · Apparatus and method for analyzing a bodily sample > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,005,443 B2 - Document ID: US12005443B2 - Status: Granted - Kind: B2 - Application number: 17/064,193 - Filed: 2020-10-06 - Published: 2021-04-08 - Granted: 2024-06-11 - Earliest claimed: 2010-12-29 - Assignees: S.D. Sight Diagnostics Ltd. - Applicants: S.D. Sight Diagnostics Ltd. - Inventors: Ido Bachelet; Joseph Joel Pollak; Daniel Levner; Yonatan Bilu; Noam Yorav-Raphael - Web page: https://patentgrep.com/patent/US12005443B2 ## CPC - B01L 3/502715: {characterised by interfacing components, e.g. fluidic, electrical, optical or mechanical interfaces} - G01N 15/1433: using image recognition - G01N 21/23: Bi-refringence - G01N 21/5907: {Densitometers} - G01N 21/6458: {Fluorescence microscopy (fluorescence microscopes per se G02B21/0076 and G02B21/16)} - B01L 2300/0816: Cards, e.g. flat sample carriers usually with flow in two horizontal directions - B01L 2300/0864: comprising only one inlet and multiple receiving wells, e.g. for separation, splitting - G01N 2015/016: {White blood cells} - G01N 2015/1006: {for cytology} - G01N 2021/5957: {using an image detector type detector, e.g. CCD} - G01N 2021/6419: {Excitation at two or more wavelengths} - G01N 2021/6421: {Measuring at two or more wavelengths} - G06V 2201/03: Recognition of patterns in medical or anatomical images - G06V 2201/04: Recognition of patterns in DNA microarrays - B01L: CHEMICAL OR PHYSICAL LABORATORY APPARATUS FOR GENERAL USE - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Apparatus and methods are described including successively acquiring a plurality of microscopic images of a portion of a blood sample, and tracking motion of pixels within the successively acquired microscopic images. Trypomastigote parasite candidates within the blood sample are identified, by identifying pixel motion that is typical of trypomastigote parasites. It is determined that the blood sample is infected with trypomastigote parasites, at least partially in response thereto. An output is generated indicating that that the blood sample is infected with trypomastigote parasites. Other applications are also described. ## Claims ### Claim 1 A method comprising: successively acquiring a plurality of microscopic images of a portion of a blood sample; tracking motion of pixels within the successively acquired microscopic images; identifying trypomastigote parasite candidates within the blood sample, by identifying pixel motion that is typical of trypomastigote parasites; determining that the blood sample is infected with trypomastigote parasites, at least partially in response thereto; and generating an output indicating that that the blood sample is infected with trypomastigote parasites. ### Claim 2 (depends on 1) The method according to claim 1, wherein identifying trypomastigote parasite candidates within the blood sample further comprises identifying entities having a worm-like shape. ### Claim 3 (depends on 1) The method according to claim 1, wherein identifying pixel motion that is typical of trypomastigote parasites comprises identifying entities that undergo a twirling motion. ### Claim 4 (depends on 1) The method according to claim 1, wherein identifying pixel motion that is typical of trypomastigote parasites comprises identifying entities that undergo a spinning motion. ### Claim 5 (depends on 1) The method according to claim 1, wherein successively acquiring the plurality of microscopic images of the portion of the blood sample comprises successively acquiring a plurality of fluorescent microscopic images of a portion of a blood sample, and wherein identifying pixel motion that is typical of trypomastigote parasites comprises identifying motion of fluorescent particles that is typical of trypomastigote parasites. ### Claim 6 (depends on 1) The method according to claim 1, wherein identifying trypomastigote parasite candidates within the blood sample comprises identifying trypomastigote parasite candidates using a multi-frame candidate construction module in which the candidates are assessed for coherency over at least some of the plurality of the microscopic images. ### Claim 7 (depends on 1) The method according to claim 1, wherein generating the output indicating that that the blood sample is infected with trypomastigote parasites comprises generating an image in which the trypomastigote parasites are visible. ### Claim 8 (depends on 1) The method according to claim 1, wherein generating the output indicating that that the blood sample is infected with trypomastigote parasites comprises generating an output indicating that a subject from whom the blood sample was taken is suffering from human African trypanosomiasis. ### Claim 9 Apparatus comprising: a microscope; and a computer processor configured to: drive the microscope to successively acquiring a plurality of microscopic images of a portion of a blood sample; track motion of pixels within the successively acquired microscopic images; identify trypomastigote parasite candidates within the blood sample, by identifying pixel motion that is typical of trypomastigote parasites; determine that the blood sample is infected with trypomastigote parasites, at least partially in response thereto; and generate an output indicating that that the blood sample is infected with trypomastigote parasites. ### Claim 10 (depends on 9) The apparatus according to claim 9, wherein the computer processor is configured to identify trypomastigote parasite candidates within the blood sample by identifying entities having a worm-like shape. ### Claim 11 (depends on 9) The apparatus according to claim 9, wherein the computer processor is configured to identify pixel motion that is typical of trypomastigote parasites by identifying entities that undergo a twirling motion. ### Claim 12 (depends on 9) The apparatus according to claim 9, wherein the computer processor is configured to identify pixel motion that is typical of trypomastigote parasites by identifying entities that undergo a spinning motion. ### Claim 13 (depends on 9) The apparatus according to claim 9, wherein the computer processor is configured to drive the microscope to successively acquire a plurality of fluorescent microscopic images of a portion of the blood sample, and wherein the computer processor is configured to identify pixel motion that is typical of trypomastigote parasites by identifying motion of fluorescent particles that is typical of trypomastigote parasites. ### Claim 14 (depends on 9) The apparatus according to claim 9, wherein the computer processor is configured to identify trypomastigote parasite candidates within the blood sample by identifying trypomastigote parasite candidates using a multi-frame candidate construction module in which the candidates are assessed for coherency over at least some of the plurality of the microscopic images. ### Claim 15 (depends on 9) The apparatus according to claim 9, wherein the computer processor is configured to generate the output indicating that that the blood sample is infected with trypomastigote parasites by generating an image in which the trypomastigote parasites are visible. ### Claim 16 (depends on 9) The apparatus according to claim 9, wherein the computer processor is configured to generate the output indicating that that the blood sample is infected with trypomastigote parasites by generating an output indicating that a subject from whom the blood sample was taken is suffering from human African trypanosomiasis.