From recent advances point of view, answer the following 1. Automation in blood bank 2. Automation in histopathology including frozen sections
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automation blood bank transfusion
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automation histopathology digital pathology frozen section
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PMID: 38318487
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PMID: 41153019
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PMID: 41359123
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PMID: 37394789
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recent advances automation blood bank 2024 2025 robotics AI
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digital pathology whole slide imaging AI diagnosis
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automated tissue processing microtomy robotic pathology
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automation histopathology laboratory 2025 digital pathology AI frozen section advances
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PMID: 40184940
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PMID: 36626280
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Recent Advances in Laboratory Automation
1. Automation in Blood Bank
A. Overview
Automation in the blood bank encompasses the entire transfusion chain - from donor management and blood collection to component processing, immunohematologic testing, inventory management, and bedside administration. Recent advances have layered robotics, AI, and sophisticated IT infrastructure over each step.
B. Blood Donation and Collection
Apheresis and Automated Component Collection
Modern apheresis machines (e.g., Terumo BCT Trima Accel, Haemonetics MCS+, Fresenius Amicus) automate single-donor plateletpheresis, plasmapheresis, and double red cell collection in a single session. Real-time monitoring for flow rates, pressure, and air detection reduces operator dependence and near-miss events.
Automated Whole Blood Processing
A 2023 systematic review (Cid et al., Vox Sanguinis 2023, PMID 36626280) comparing automated vs. semi-automated whole blood processing systems found that full automation:
- Reduced run-time from 92 to 76 minutes per batch
- Improved haemoglobin recovery in red cell concentrates (RCCs)
- Reduced discard due to bag ruptures (1.2% → 0.1%), low-volume RCCs (0.5% → 0.03%), and haemolytic plasma (2.1% → 0.6%)
- Allowed the same FTE staffing to handle greater throughput
- Reduced device footprint to as little as 1.13 m²
In 2024, Haemonetics launched a fully automated blood processing unit and Terumo introduced an advanced blood separation system with real-time quality control monitoring.
C. Immunohematology Testing (Blood Grouping & Compatibility)
Traditional tube-based agglutination testing has been largely replaced or supplemented by:
Automated Column Agglutination Technology (CAT) / Gel Cards
Systems such as the Bio-Rad IH-500 and Ortho Vision Analyzer automate ABO/Rh typing, antibody screening, DAT (direct antiglobulin test), antigen typing, crossmatching, dilution, and reflex testing on gel/card platforms. Per the Tietz Textbook of Laboratory Medicine (7th ed.), "both small- and large-scale systems are now available to automate many of these techniques... In general, automated blood bank systems are as reliable and sensitive as routine manual testing. Advantages include less opportunity for processing and transcription errors."
Solid-Phase Red Cell Adherence (SPRCA)
Platforms like the Immucor Galileo Neo handle large volumes with full walk-away capability - antibody identification, crossmatch, and DAT with reduced hands-on time.
Molecular Blood Typing (Genotyping)
High-throughput genotyping platforms (e.g., Immucor PreciseType, Bio-Rad RHD BeadChip) enable simultaneous typing for 37+ red cell antigens from a single sample. This is especially valuable for:
- Patients with recent transfusions (where serologic typing is confounded)
- Sickle cell disease patients requiring extended phenotype matching
- Weak/variant D typing decisions
- Rare blood type registries
D. AI and IT Systems in Transfusion Safety
A systematic mapping study (Elhaj et al., J Multidiscip Healthc 2024, PMID 38318487) reviewed the landscape of blood bank process automation and found that automation consistently improves transfusion safety, reduces cycle time, and cuts human error - but noted a significant gap in formal process modeling frameworks.
Key IT-driven advances include:
| Technology | Function |
|---|---|
| Barcode / RFID labeling | Tracks each unit from collection to transfusion; prevents wrong-blood-in-tube |
| Electronic crossmatch | Eliminates serologic crossmatch when patient has 2 concordant ABO/Rh typings and negative antibody screen |
| Blood Management Software (e.g., Mediware BloodSafe, Haemonetics SafeTrace) | Integrates donor records, inventory, expiry alerts, and crossmatch results |
| Personal Digital Assistants (PDA) / bedside scanning | Barcode matching at point-of-care to confirm patient - unit identity before transfusion |
| Adaptive clinical decision support | Flags orders that exceed transfusion thresholds; provides restrictive transfusion reminders |
A 2025 review (Chen et al., Comput Biol Med 2025, PMID 40184940) documented that an integrated IT system with barcode matching and PDA tracking over 2019-2024 reduced red cell waste to 1.0% and drove transfusion reaction monitoring with near-real-time data.
E. AI in Donor Management and Demand Forecasting
A 2026 narrative review (Badawi, Vox Sanguinis 2026, PMID 41153019) identified several AI applications:
- Robotic Process Automation (RPA): Automates repetitive administrative tasks - donor eligibility checks, appointment scheduling, reminder communications - freeing staff for clinical roles
- Machine Learning for Demand Forecasting: LSTM networks and other models analyze donation history, seasonal variation, and hospital consumption data to predict blood shortfalls days to weeks in advance
- AI-powered Chatbots: Improve donor communication and retention by personalizing engagement
- Rare Donor Identification: Deep learning models identify rare blood type donors from large genotype datasets
- Predictive deferral models: ML models using SHAP (SHapley Additive exPlanations) values flag donors at risk of deferral (low haemoglobin, travel history), reducing unnecessary deferral visits
The AI-driven transfusion decision support market was estimated at USD 1.21 billion in 2024, projected to reach USD 6.38 billion by 2033. The broader AI-integrated blood analyzer market is projected to expand at a CAGR of 21.3% through 2035.
F. Total Laboratory Automation (TLA) in the Blood Bank
TLA integrates pre-analytical specimen handling (pneumatic tube systems, automated centrifugation, decapping, aliquoting) with post-analytical archiving under a single laboratory information system (LIS). Blood bank modules are increasingly integrated into hospital-wide TLA platforms (e.g., Beckman Coulter PowerExpress, Roche cobas connection modules), enabling:
- Reflexive testing without manual re-handling
- Automated sample routing to immunohematology vs. serology vs. NAT testing based on specimen type and order
- Night/weekend reduced-staffing walk-away operation
2. Automation in Histopathology (Including Frozen Sections)
A. Pre-Analytical Automation
Digital Order Entry and Specimen Tracking
Modern pathology workflows begin at the clinical site with digital order entry systems that generate cassette labels, barcodes, and requisitions automatically. Tracking systems follow the specimen from operating room / biopsy site through fixation, processing, embedding, sectioning, staining, and archiving - eliminating manual transcription errors.
A 2026 review (Herbst & Pocha, Pathologie 2026, PMID 41359123) described end-to-end automation covering "all aspects from clinical sampling, cassette printing, and automated processing and embedding to fully automated microtomy and slide printing as well as digitally supported archiving of blocks and slides."
Automated Tissue Processors
Next-generation processors (Leica ASP6025, Sakura Tissue-Tek Xpress x120) use microwave-accelerated processing to reduce fixation-to-slide time from overnight to 2-4 hours. Vacuum/pressure cycles improve reagent penetration. Some systems integrate carousel loading and unloading with robotic arms.
Automated Embedding Centers and Coverslipper
Robotic embedding systems (Sakura TEC 6 Cryo) allow standardized paraffin block orientation. Automated coverslippers apply uniform mounting media and coverslips, reducing air bubbles and improving scan quality.
B. Automated Microtomy
One of the most challenging steps to automate has been microtomy, given the haptic feedback required. Recent advances:
- Semi-automated microtomes (Leica RM2255, Microm HM355S) with programmable section thickness, auto-advance, and electronic trim cycles
- Fully automated microtomy systems (e.g., Kurabo AutoSlicer): robotic sectioning and section transfer to water baths with minimal human intervention, now reaching production-level throughput in high-volume centers
- These technologies are explicitly described in the 2026 Pathologie review as enabling "fully automated microtomy and slide printing" as part of a digital pathology pipeline
C. Automated Staining
Hematoxylin & Eosin (H&E) Autostainers
Linear and carousel staining platforms (Leica ST5020, Sakura Tissue-Tek Prisma Plus) process 200+ slides/hour with standardized staining protocols and LIS connectivity for protocol selection by specimen type.
Immunohistochemistry (IHC) and In-Situ Hybridization (ISH) Automation
Fully automated IHC platforms (Ventana BenchMark Ultra, Leica BOND-III, Dako Omnis) perform antigen retrieval, primary antibody incubation, detection, and counterstaining without manual steps. Advantages include:
- Reproducible staining quality across batches
- Closed, validated reagent systems
- LIS-driven protocol selection per order
- Multiplex IHC (simultaneous detection of 4-8 markers on a single slide) is now entering clinical practice
D. Digital Pathology - Whole Slide Imaging (WSI)
WSI scanners (Leica Aperio GT 450, Philips IntelliSite, 3DHistech Pannoramic 1000) digitize glass slides at 20x-40x magnification producing gigapixel images. These are stored in image management systems (DIMS/LIMS) and form the substrate for:
- Remote sign-out and tele-pathology
- AI-based image analysis
- Educational archives
- Second-opinion consultation without physical slide transport
A 2025 review (Jain et al., Int J Surg Pathol 2024, PMID 37437093) noted WSI as the foundation for emerging AI applications in cancer detection, grading, and biomarker quantification.
As of 2025, digital slide scanning adoption is at 35-45% (medium-high maturity), while AI-assisted primary diagnosis is at 8-14% (emerging). The Leica Aperio Computational Pathology ecosystem and similar platforms are specifically designed for end-to-end integration.
E. AI and Deep Learning in Histopathology
Cancer Detection and Classification
Convolutional neural networks (CNNs) and transformer-based models trained on WSI now achieve pathologist-level or better performance for:
- Prostate cancer Gleason grading (FDA-cleared: Paige Prostate, Ibex Galen)
- Breast cancer ER/PR quantification (replacing manual H-score)
- Colorectal cancer subtyping
- Mesothelioma vs. adenocarcinoma discrimination
A 2025 review (Dang et al., Lab Invest 2025, PMID 40306572) highlighted deep learning on WSI for multi-task cancer characterization beyond histomorphology - including molecular subtype prediction directly from H&E slides.
Biomarker Quantification
AI algorithms automatically quantify:
- Ki-67 proliferation index
- PD-L1 expression (TPS, CPS scores)
- HER2 membrane scoring
- Tumour infiltrating lymphocytes (TILs) - replacing labour-intensive manual counts
Prognostic/Predictive Modeling
Foundation models (e.g., UNI, CONCH, PLIP) trained on millions of pathology tiles are being fine-tuned for specific tasks. Predicting treatment response (e.g., to immunotherapy or chemotherapy) directly from H&E slides - without additional molecular tests - is identified as the most promising near-term application in expert surveys.
F. AI in Frozen Sections Specifically
A systematic review (Gorman et al., J Cutan Pathol 2023, PMID 37394789) analyzed 18 ML models trained or tested specifically on frozen section images. Key findings:
- CNNs consistently outperform other architectures on frozen section interpretation
- Human-AI collaboration outperforms either alone: when pathologists could view model output, diagnostic accuracy exceeded that of the model or pathologist working independently
- Frozen section-trained models generalize better: models trained on frozen tissue performed well across slide preparations, but formalin-fixed paraffin-embedded (FFPE)-only trained models performed significantly worse on frozen sections - underscoring the need for modality-specific training
- Applications tested include Mohs surgery margin assessment, brain tumor intraoperative diagnosis (glioma grading), and lymph node metastasis detection
Virtual Frozen Sections / Rapid WSI
A major 2025 development at Washington University (St. Louis) used Grundium compact scanners for rapid on-site evaluation (ROSE) and frozen section digital review across 6 locations simultaneously - matching traditional diagnostic accuracy while reducing turnaround time and enabling remote pathologist sign-out for intraoperative consultations.
Stimulated Raman Histology (SRH)
SRH is an emerging label-free optical technique that generates pseudo-H&E-quality images from fresh, unprocessed tissue in minutes - without freezing or staining. When combined with CNNs (e.g., NIO system by Invenio Imaging), SRH enables near-real-time intraoperative brain tumor classification, directly challenging the traditional cryostat-based frozen section workflow. Clinical trials have shown non-inferiority to conventional frozen sections for glioma diagnosis.
G. AI-Assisted Workflow Management
- Automated case triage and prioritization: AI pre-screens incoming cases and routes urgent malignancies to the top of the worklist
- Quality control: ML models flag out-of-focus slides, tissue folds, staining artifacts, and insufficient tissue before a pathologist reviews the case
- Integrated reporting: AI findings are embedded into structured pathology report templates in the LIS, with structured data output for precision oncology tumor boards
Summary Table
| Domain | Key Recent Advance |
|---|---|
| Blood bank - processing | Fully automated whole blood separation; reduced discard, improved yields (PMID 36626280) |
| Blood bank - immunohematology | High-throughput CAT/gel-card systems; automated electronic crossmatch |
| Blood bank - molecular typing | 37+ antigen genotyping panels on single sample |
| Blood bank - IT/safety | Barcode + PDA bedside matching; adaptive clinical decision support (PMID 40184940) |
| Blood bank - AI/donor | RPA + ML for demand forecasting, chatbot-based donor retention (PMID 41153019) |
| Histopathology - processing | Rapid microwave tissue processors (2-4 hr turnaround); robotic embedding |
| Histopathology - microtomy | Fully automated robotic microtomy (PMID 41359123) |
| Histopathology - staining | Automated IHC/ISH with LIS-driven protocols; multiplex IHC |
| Histopathology - digital | WSI at 35-45% adoption; FDA-cleared AI for prostate and breast pathology |
| Frozen sections - AI | CNNs outperform pathologists alone; human-AI hybrid best (PMID 37394789) |
| Frozen sections - technology | SRH (label-free optical imaging); compact WSI scanners for remote frozen section review |
Key references:
- Tietz Textbook of Laboratory Medicine, 7th ed. - Chapter 29, Transfusion Medicine (p. 893-896)
- Cid et al. Vox Sanguinis 2023 - Automated whole blood processing (PMID 36626280)
- Elhaj et al. J Multidiscip Healthc 2024 - Blood banking automation mapping (PMID 38318487)
- Badawi. Vox Sanguinis 2026 - AI in donor management (PMID 41153019)
- Chen et al. Comput Biol Med 2025 - IT and transfusion safety (PMID 40184940)
- Herbst & Pocha. Pathologie 2026 - Pathology lab automation (PMID 41359123)
- Gorman et al. J Cutan Pathol 2023 - AI in frozen sections (PMID 37394789)
- Dang et al. Lab Invest 2025 - Deep learning in WSI (PMID 40306572)
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