{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T16:14:13Z","timestamp":1785428053003,"version":"3.56.0"},"reference-count":40,"publisher":"American Society for Microbiology","issue":"4","license":[{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.asm.org\/non-commercial-tdm-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82202650"],"award-info":[{"award-number":["82202650"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019081","name":"Science and Technology Program of Hunan Province","doi-asserted-by":"publisher","award":["2021SK53523"],"award-info":[{"award-number":["2021SK53523"]}],"id":[{"id":"10.13039\/501100019081","id-type":"DOI","asserted-by":"publisher"}]},{"name":"HSTD | Natural Science Foundation of Hunan Province","award":["2023JJ60424"],"award-info":[{"award-number":["2023JJ60424"]}]},{"name":"HSTD | Natural Science Foundation of Hunan Province","award":["2024JJ8201"],"award-info":[{"award-number":["2024JJ8201"]}]},{"DOI":"10.13039\/100017695","name":"Health Commission of Hunan Province","doi-asserted-by":"publisher","award":["D202311007271"],"award-info":[{"award-number":["D202311007271"]}],"id":[{"id":"10.13039\/100017695","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.asm.org"],"crossmark-restriction":true},"short-container-title":["J Clin Microbiol"],"published-print":{"date-parts":[[2026,4,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title\/>\n                    <jats:p>\n                      Rapid and accurate identification of\n                      <jats:italic toggle=\"yes\">Aspergillus<\/jats:italic>\n                      species in clinical microbiology laboratories is crucial for aspergillosis diagnosis and antifungal therapy. However, traditional methods still face challenges in distinguishing phylogenetically related species due to their morphological similarities. This study presents FungalNet, a deep learning model integrating ResNet-50 architecture with Focal Loss algorithm, specifically designed to enhance feature extraction for\n                      <jats:italic toggle=\"yes\">Aspergillus<\/jats:italic>\n                      identification. A total of 12,000 high-resolution images were obtained from lactophenol cotton blue-stained slide preparations under a 100\u00d7 oil immersion objective, among which 311 images were excluded through a novel quality control approach combining fivefold cross-validation and expert manual review. The performance of four deep learning models (FungalNet and three established models) for identifying\n                      <jats:italic toggle=\"yes\">Aspergillus<\/jats:italic>\n                      species and sections was evaluated using the remaining 11,689 qualified images. FungalNet demonstrated superior classification performance, achieving overall accuracies of 98.45% and 97.85% at the section and species levels, respectively. These results indicate that FungalNet shows significant promise for rapid and accurate identification of\n                      <jats:italic toggle=\"yes\">Aspergillus<\/jats:italic>\n                      species. With further optimization and multicenter validation, this tool could potentially be integrated into routine diagnostic workflows to enhance the efficiency and reliability of fungal identification in clinical settings.\n                    <\/jats:p>\n                    <jats:sec>\n                      <jats:title>IMPORTANCE<\/jats:title>\n                      <jats:p>\n                        This study integrates microscopic morphology identification with deep learning to address the challenge of accurate\n                        <jats:italic toggle=\"yes\">Aspergillus<\/jats:italic>\n                        species identification. Twelve clinically isolated\n                        <jats:italic toggle=\"yes\">Aspergillus<\/jats:italic>\n                        species belonging to eight different sections were included. From touch-tape slide preparations with lactophenol cotton blue staining under a 100\u00d7 oil immersion objective, 11,689 qualified images were collected and analyzed using FungalNet (our proposed model) along with three established models (GoogLeNet, ResNet-50, and Xception). The results showed that FungalNet demonstrated superior performance in\n                        <jats:italic toggle=\"yes\">Aspergillus<\/jats:italic>\n                        identification, achieving the highest classification accuracy at both section (98.45%) and species (97.85%) levels. Given its rapid turnaround time and cost-effectiveness, this AI-based image analysis approach shows promising potential for the rapid and accurate identification of\n                        <jats:italic toggle=\"yes\">Aspergillus<\/jats:italic>\n                        species in clinical microbiology laboratories.\n                      <\/jats:p>\n                    <\/jats:sec>\n                  <\/jats:sec>","DOI":"10.1128\/jcm.00012-26","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T14:03:17Z","timestamp":1772200997000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["Identification of\n                    <i>Aspergillus<\/i>\n                    at section and species levels by artificial intelligence-based microscopic morphology image recognition"],"prefix":"10.1128","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4740-0872","authenticated-orcid":true,"given":"Meng","family":"Tan","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/053v2gh09","id-type":"ROR","asserted-by":"publisher"}],"name":"Department of Laboratory Medicine, The Second Xiangya Hospital, Central South University","place":["Changsha, China"]},{"name":"Department of Laboratory Medicine, Hunan Provincial Rehabilitation Hospital","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6657-4793","authenticated-orcid":true,"given":"Zhe","family":"Guo","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/053v2gh09","id-type":"ROR","asserted-by":"publisher"}],"name":"Department of Laboratory Medicine, The Second Xiangya Hospital, Central South University","place":["Changsha, 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