{"id":"679918cb-d701-469b-a30e-306d1aa118b1","arxiv_id":"2604.24685","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":3.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Aycromo is an open-source desktop platform that integrates pre-trained deep learning models such as YOLOv11 for automatic chromosome detection in metaphase images, achieving 99.40% mAP@50 on the CRCN-NE dataset while reducing per-slide analysis time to seconds.","lead":"The paper presents Aycromo, an open-source desktop platform built with Electron and ONNX Runtime that lets users load deep learning models to detect chromosomes in metaphase cell images and manually correct results. A smart generalist might read it to understand how AI tools can be packaged for practical use in medical labs without requiring programming expertise.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"99.40% mAP@50 on CRCN-NE alone does not support generalization or clinical readiness without cross-dataset testing or workflow timing details.","rationale":"The reader's weakest assumption (generalization and missing clinical validation) directly identifies the load-bearing gap. The preliminary nature of the experiments is already acknowledged in the abstract, so the concern reinforces rather than alters the UNVERDICTED stance pending external checks.","tokens_in":1675,"tokens_out":313,"duration_ms":20864,"concrete_test":"Run the released YOLOv11 weights on an independent set of 200+ metaphase images from a second cytogenetics center (different scanner and staining protocol); report mAP@50 and the fraction of detections requiring manual correction. If mAP falls below 92% or correction time exceeds 30 s per slide on average, the headline performance claim does not transfer.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim hinges on YOLOv11 delivering near-perfect detection that enables a usable desktop platform. This rests on the untested assumption that CRCN-NE images are representative of real clinical variability (staining, resolution, cell density, artifacts). No evidence is given that the model was evaluated on external metaphase collections, that mAP@50 correlates with downstream karyotyping accuracy after manual correction, or that the reported seconds-per-slide includes the interactive correction step. Without these, the performance number cannot carry the claim that the tool reduces analysis time in practice.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript introduces Aycromo, an open-source desktop platform built with Electron and ONNX Runtime for AI-assisted chromosome detection and karyotyping in metaphase images. It integrates pre-trained deep learning models (with YOLOv11 highlighted), an interactive annotation interface for manual corrections, and a benchmarking module for comparing architectures, all accessible without command-line use. Preliminary experiments on the CRCN-NE dataset report that YOLOv11 achieves 99.40% mAP@50 while reducing per-slide analysis time to seconds.","tokens_in":1795,"tokens_out":421,"duration_ms":19930,"significance":"If the performance and usability claims are substantiated with additional validation, the work would be significant for bridging research prototypes and clinical tools in cytogenetics. The open-source release, desktop implementation, and integrated benchmarking module are explicit strengths that could facilitate adoption and further model comparisons by practitioners.","major_comments":[{"comment":"Abstract: The central performance claim of 99.40% mAP@50 for YOLOv11 is presented without any details on dataset splits, training procedure, baseline comparisons, error analysis, or statistical significance. This information is load-bearing for assessing whether the result supports the claim that the platform reduces analysis time in practice.","section":"Abstract"},{"comment":"Experiments section (preliminary results): No cross-dataset evaluation or testing on external metaphase image collections is reported. This is required to substantiate generalization, as the platform's utility rests on the untested assumption that CRCN-NE images capture clinical variability in staining, resolution, cell density, and artifacts.","section":"Experiments"}],"minor_comments":[{"comment":"Abstract: The final sentence is grammatically incomplete ('reduces per-slide analysis to seconds') and should be revised for clarity, e.g., by specifying measured times or including the interactive correction step in the timing.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive comments on our manuscript. We provide point-by-point responses to the major comments below, indicating where revisions will be made to the manuscript.","responses":[{"response":"The abstract is constrained by length, so detailed experimental information is provided in the Experiments section. To address the referee's concern, we will revise the abstract to include a short statement on the dataset and the preliminary nature of the results. We will also review and enhance the Experiments section to ensure all relevant details on splits, training, and comparisons are clearly presented.","revision_made":"partial","referee_comment":"[Abstract] Abstract: The central performance claim of 99.40% mAP@50 for YOLOv11 is presented without any details on dataset splits, training procedure, baseline comparisons, error analysis, or statistical significance. This information is load-bearing for assessing whether the result supports the claim that the platform reduces analysis time in practice."},{"response":"We agree that cross-dataset evaluation would provide stronger evidence of generalization. Since the manuscript reports preliminary results on the CRCN-NE dataset, external testing was not included. In the revision, we will add a new subsection or paragraph in the Discussion or a Limitations section to explicitly note this limitation and discuss the potential variability in clinical images. This will clarify the scope of the current claims without altering the reported results.","revision_made":"partial","referee_comment":"[Experiments] Experiments section (preliminary results): No cross-dataset evaluation or testing on external metaphase image collections is reported. This is required to substantiate generalization, as the platform's utility rests on the untested assumption that CRCN-NE images capture clinical variability in staining, resolution, cell density, and artifacts."}],"tokens_in":1310,"tokens_out":387,"duration_ms":28221,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"Aycromo is an open-source desktop platform that wraps a YOLOv11 model for detecting chromosomes in metaphase images. The authors built it with Electron and ONNX Runtime so users can load models, run benchmarks inside the app, and correct detections through an interactive interface without touching the command line.","headline":"Aycromo packages YOLOv11 into an Electron desktop app for chromosome detection, but the 99.4% mAP claim rests on one dataset with almost no experimental details.","tokens_in":2312,"tokens_out":143,"would_cite":false,"duration_ms":27185,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"An open-source desktop platform uses YOLOv11 to detect chromosomes in metaphase images at 99.40% accuracy while cutting per-slide analysis to seconds.","keywords":["chromosome detection","metaphase images","deep learning","YOLO","cytogenetic analysis","open-source platform","karyotyping","genetic diagnosis"],"falsifier":"A test of the platform on metaphase images from multiple independent clinical labs outside CRCN-NE that shows mAP@50 dropping below 90%, or cytogeneticist feedback revealing that the interface still requires command-line workarounds.","tokens_in":2558,"feed_emoji":"🧬","tokens_out":660,"duration_ms":24359,"temperature":0.7,"pith_summary":"The paper introduces Aycromo, an open-source desktop application for AI-assisted chromosome detection in cytogenetic workflows. Built with Electron and ONNX Runtime, it lets users load pre-trained models, benchmark different architectures in one interface, and manually correct detections without command-line steps. On the CRCN-NE dataset, YOLOv11 reaches 99.40% mAP@50, addressing the multi-day manual karyotyping process that currently depends on scarce specialists for genetic disease diagnosis.","feed_headline":"Open-source tool detects chromosomes with 99.4% accuracy in seconds","feed_subtitle":"Aycromo desktop app lets cytogeneticists load models, benchmark architectures, and correct detections without coding.","key_machinery":"The Aycromo platform built on Electron for the desktop GUI and ONNX Runtime for model inference, featuring an integrated benchmarking module and interactive annotation interface that supports model loading and manual correction of detections.","core_discovery":"Aycromo is an open-source desktop platform for automatic chromosome detection in metaphase images based on deep learning. It integrates models such as YOLOv11, which achieves 99.40% mAP@50 on the CRCN-NE dataset, and supplies a graphical interface for loading models, comparing architectures through an integrated benchmarking module, and interactively correcting detections, all without command-line interaction. The platform reduces per-slide analysis to seconds.","pith_inferences":["Wider adoption could reduce dependence on highly trained specialists for routine genetic screening.","Integration with existing laboratory information systems would be a logical next step to embed the tool in clinical pipelines.","Performance on images from underrepresented populations or imaging equipment remains an open question that future users could test directly."],"forward_implications":["Per-slide chromosome analysis time drops from days to seconds for users of the platform.","Different deep learning architectures can be directly compared and selected inside the same graphical environment.","Manual correction of detections becomes feasible within the workflow, allowing accuracy gains in practice.","Open-source availability removes barriers for labs to deploy and extend automated karyotyping tools."],"fun_headline_variants":["Desktop app detects chromosomes in seconds at 99.4% accuracy","Aycromo open-source platform automates metaphase analysis","GUI tool enables AI model benchmarking without coding","YOLOv11 model detects chromosomes with 99.4% mAP@50"],"cache_read_input_tokens":64,"weakest_assumption_plain":"High accuracy on the CRCN-NE dataset will hold for diverse clinical metaphase images from varied sources, and cytogeneticists will adopt the desktop interface without further clinical validation or integration testing.","fun_headline_variants_meta":{"raw":{"variants":["Desktop app detects chromosomes in seconds at 99.4% accuracy","Aycromo open-source platform automates metaphase analysis","GUI tool enables AI model benchmarking without coding","YOLOv11 model detects chromosomes with 99.4% mAP@50"]},"model":"grok-4.3","cost_usd":0.008864,"raw_usage":{"total_tokens":3881,"prompt_tokens":617,"num_sources_used":0,"completion_tokens":70,"cost_in_usd_ticks":88640500,"prompt_tokens_details":{"text_tokens":617,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":3194,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":617,"tokens_out":70,"duration_ms":37646,"temperature":1.0,"reasoning_tokens":3194,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-08T04:31:50.499357+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A test of the platform on metaphase images from multiple independent clinical labs outside CRCN-NE that shows mAP@50 dropping below 90%, or cytogeneticist feedback revealing that the interface still requires command-line workarounds.","supporting_citations":[],"review_version":1}