Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometrium
Background: The human endometrium undergoes a monthly cycle of tissue growth and degeneration. During the mid-secretory phase, the endometrium establishes an optimal niche for embryo implantation by regulating cellular composition (e.g., epithelial and stromal cells) and differentiation. Impaired en...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Journal of Pathology Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353924000038 |
| _version_ | 1797281223856357376 |
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| author | Seungbaek Lee Riikka K. Arffman Elina K. Komsi Outi Lindgren Janette Kemppainen Keiu Kask Merli Saare Andres Salumets Terhi T. Piltonen |
| author_facet | Seungbaek Lee Riikka K. Arffman Elina K. Komsi Outi Lindgren Janette Kemppainen Keiu Kask Merli Saare Andres Salumets Terhi T. Piltonen |
| author_sort | Seungbaek Lee |
| collection | DOAJ |
| description | Background: The human endometrium undergoes a monthly cycle of tissue growth and degeneration. During the mid-secretory phase, the endometrium establishes an optimal niche for embryo implantation by regulating cellular composition (e.g., epithelial and stromal cells) and differentiation. Impaired endometrial development observed in conditions such as polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) contributes to infertility. Surprisingly, despite the importance of the endometrial lining properly developing prior to pregnancy, precise measures of endometrial cellular composition in these two infertility-associated conditions are entirely lacking. Additionally, current methods for measuring the epithelial and stromal area have limitations, including intra- and inter-observer variability and efficiency. Methods: We utilized a deep-learning artificial intelligence (AI) model, created on a cloud-based platform and developed in our previous study. The AI model underwent training to segment both areas populated by epithelial and stromal endometrial cells. During the training step, a total of 28.36 mm2 areas were annotated, comprising 2.56 mm2 of epithelium and 24.87 mm2 of stroma. Two experienced pathologists validated the performance of the AI model. 73 endometrial samples from healthy control women were included in the sample set to establish cycle phase-dependent dynamics of the endometrial epithelial-to-stroma ratio from the proliferative (PE) to secretory (SE) phases. In addition, 91 samples from PCOS cases, accounting for the presence or absence of ovulation and representing all menstrual cycle phases, and 29 samples from RIF patients on day 5 after progesterone administration in the hormone replacement treatment cycle were also included and analyzed in terms of cellular composition. Results: Our AI model exhibited reliable and reproducible performance in delineating epithelial and stromal compartments, achieving an accuracy of 92.40% and 99.23%, respectively. Moreover, the performance of the AI model was comparable to the pathologists’ assessment, with F1 scores exceeding 82% for the epithelium and >96% for the stroma. Next, we compared the endometrial epithelial-to-stromal ratio during the menstrual cycle in women with PCOS and in relation to endometrial receptivity status in RIF patients. The ovulatory PCOS endometrium exhibited epithelial cell proportions similar to those of control and healthy women’s samples in every cycle phase, from the PE to the late SE, correlating with progesterone levels (control SE, r2 = 0.64, FDR < 0.001; PCOS SE, r2 = 0.52, FDR < 0.001). The mid-SE endometrium showed the highest epithelial percentage compared to both the early and late SE endometrium in both healthy women and PCOS patients. Anovulatory PCOS cases showed epithelial cellular fractions comparable to those of PCOS cases in the PE (Anovulatory, 14.54%; PCOS PE, 15.56%, p = 1.00). We did not observe significant differences in the epithelial-to-stroma ratio in the hormone-induced endometrium in RIF patients with different receptivity statuses. Conclusion: The AI model rapidly and accurately identifies endometrial histology features by calculating areas occupied by epithelial and stromal cells. The AI model demonstrates changes in epithelial cellular proportions according to the menstrual cycle phase and reveals no changes in epithelial cellular proportions based on PCOS and RIF conditions. In conclusion, the AI model can potentially improve endometrial histology assessment by accelerating the analysis of the cellular composition of the tissue and by ensuring maximal objectivity for research and clinical purposes. |
| first_indexed | 2024-03-07T16:54:17Z |
| format | Article |
| id | doaj.art-eaa925db27354f4cb67635506b8628f3 |
| institution | Directory Open Access Journal |
| issn | 2153-3539 |
| language | English |
| last_indexed | 2024-03-07T16:54:17Z |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj.art-eaa925db27354f4cb67635506b8628f32024-03-03T04:29:40ZengElsevierJournal of Pathology Informatics2153-35392024-12-0115100364Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometriumSeungbaek Lee0Riikka K. Arffman1Elina K. Komsi2Outi Lindgren3Janette Kemppainen4Keiu Kask5Merli Saare6Andres Salumets7Terhi T. Piltonen8Department of Obstetrics and Gynaecology, Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu 90220, Finland; Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu 50406, EstoniaDepartment of Obstetrics and Gynaecology, Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu 90220, FinlandDepartment of Obstetrics and Gynaecology, Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu 90220, FinlandDepartment of Pathology, Oulu University Hospital, Cancer and Translational Medicine Research Unit, University of Oulu, Oulu 90220, FinlandDepartment of Pathology, Oulu University Hospital, Cancer and Translational Medicine Research Unit, University of Oulu, Oulu 90220, FinlandDepartment of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu 50406, Estonia; Competence Centre on Health Technologies, Tartu 51014, EstoniaDepartment of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu 50406, Estonia; Competence Centre on Health Technologies, Tartu 51014, EstoniaDepartment of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu 50406, Estonia; Competence Centre on Health Technologies, Tartu 51014, Estonia; Division of Obstetrics and Gynaecology, Department of Clinical Science, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm 14152, SwedenDepartment of Obstetrics and Gynaecology, Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu 90220, Finland; Corresponding author.Background: The human endometrium undergoes a monthly cycle of tissue growth and degeneration. During the mid-secretory phase, the endometrium establishes an optimal niche for embryo implantation by regulating cellular composition (e.g., epithelial and stromal cells) and differentiation. Impaired endometrial development observed in conditions such as polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) contributes to infertility. Surprisingly, despite the importance of the endometrial lining properly developing prior to pregnancy, precise measures of endometrial cellular composition in these two infertility-associated conditions are entirely lacking. Additionally, current methods for measuring the epithelial and stromal area have limitations, including intra- and inter-observer variability and efficiency. Methods: We utilized a deep-learning artificial intelligence (AI) model, created on a cloud-based platform and developed in our previous study. The AI model underwent training to segment both areas populated by epithelial and stromal endometrial cells. During the training step, a total of 28.36 mm2 areas were annotated, comprising 2.56 mm2 of epithelium and 24.87 mm2 of stroma. Two experienced pathologists validated the performance of the AI model. 73 endometrial samples from healthy control women were included in the sample set to establish cycle phase-dependent dynamics of the endometrial epithelial-to-stroma ratio from the proliferative (PE) to secretory (SE) phases. In addition, 91 samples from PCOS cases, accounting for the presence or absence of ovulation and representing all menstrual cycle phases, and 29 samples from RIF patients on day 5 after progesterone administration in the hormone replacement treatment cycle were also included and analyzed in terms of cellular composition. Results: Our AI model exhibited reliable and reproducible performance in delineating epithelial and stromal compartments, achieving an accuracy of 92.40% and 99.23%, respectively. Moreover, the performance of the AI model was comparable to the pathologists’ assessment, with F1 scores exceeding 82% for the epithelium and >96% for the stroma. Next, we compared the endometrial epithelial-to-stromal ratio during the menstrual cycle in women with PCOS and in relation to endometrial receptivity status in RIF patients. The ovulatory PCOS endometrium exhibited epithelial cell proportions similar to those of control and healthy women’s samples in every cycle phase, from the PE to the late SE, correlating with progesterone levels (control SE, r2 = 0.64, FDR < 0.001; PCOS SE, r2 = 0.52, FDR < 0.001). The mid-SE endometrium showed the highest epithelial percentage compared to both the early and late SE endometrium in both healthy women and PCOS patients. Anovulatory PCOS cases showed epithelial cellular fractions comparable to those of PCOS cases in the PE (Anovulatory, 14.54%; PCOS PE, 15.56%, p = 1.00). We did not observe significant differences in the epithelial-to-stroma ratio in the hormone-induced endometrium in RIF patients with different receptivity statuses. Conclusion: The AI model rapidly and accurately identifies endometrial histology features by calculating areas occupied by epithelial and stromal cells. The AI model demonstrates changes in epithelial cellular proportions according to the menstrual cycle phase and reveals no changes in epithelial cellular proportions based on PCOS and RIF conditions. In conclusion, the AI model can potentially improve endometrial histology assessment by accelerating the analysis of the cellular composition of the tissue and by ensuring maximal objectivity for research and clinical purposes.http://www.sciencedirect.com/science/article/pii/S2153353924000038Artificial intelligenceEndometriumPolycystic ovary syndromeRecurrent implantation failureIVFComputational histology |
| spellingShingle | Seungbaek Lee Riikka K. Arffman Elina K. Komsi Outi Lindgren Janette Kemppainen Keiu Kask Merli Saare Andres Salumets Terhi T. Piltonen Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometrium Journal of Pathology Informatics Artificial intelligence Endometrium Polycystic ovary syndrome Recurrent implantation failure IVF Computational histology |
| title | Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometrium |
| title_full | Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometrium |
| title_fullStr | Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometrium |
| title_full_unstemmed | Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometrium |
| title_short | Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometrium |
| title_sort | dynamic changes in ai based analysis of endometrial cellular composition analysis of pcos and rif endometrium |
| topic | Artificial intelligence Endometrium Polycystic ovary syndrome Recurrent implantation failure IVF Computational histology |
| url | http://www.sciencedirect.com/science/article/pii/S2153353924000038 |
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