Published on in Vol 8, No 4 (2020): April

A Hematologist-Level Deep Learning Algorithm (BMSNet) for Assessing the Morphologies of Single Nuclear Balls in Bone Marrow Smears: Algorithm Development

A Hematologist-Level Deep Learning Algorithm (BMSNet) for Assessing the Morphologies of Single Nuclear Balls in Bone Marrow Smears: Algorithm Development

A Hematologist-Level Deep Learning Algorithm (BMSNet) for Assessing the Morphologies of Single Nuclear Balls in Bone Marrow Smears: Algorithm Development

Journals

  1. Sirinukunwattana K, Aberdeen A, Theissen H, Sousos N, Psaila B, Mead A, Turner G, Rees G, Rittscher J, Royston D. Artificial intelligence–based morphological fingerprinting of megakaryocytes: a new tool for assessing disease in MPN patients. Blood Advances 2020;4(14):3284 View
  2. Jiang X, Zeng Y, Xiao S, He S, Ye C, Qi Y, Zhao J, Wei D, Hu M, Chen F. Automatic Detection of Coronary Metallic Stent Struts Based on YOLOv3 and R-FCN. Computational and Mathematical Methods in Medicine 2020;2020:1 View
  3. Mori J, Kaji S, Kawai H, Kida S, Tsubokura M, Fukatsu M, Harada K, Noji H, Ikezoe T, Maeda T, Matsuda A. Assessment of dysplasia in bone marrow smear with convolutional neural network. Scientific Reports 2020;10(1) View
  4. Smith M, Westerling-Bui T, Wilcox A, Schwartz J. Screening For Bone Marrow Cellularity Changes in Cynomolgus Macaques in Toxicology Safety Studies Using Artificial Intelligence Models. Toxicologic Pathology 2021;49(4):905 View
  5. Walter W, Haferlach C, Nadarajah N, Schmidts I, Kühn C, Kern W, Haferlach T. How artificial intelligence might disrupt diagnostics in hematology in the near future. Oncogene 2021;40(25):4271 View
  6. Tayebi R, Mu Y, Dehkharghanian T, Ross C, Sur M, Foley R, Tizhoosh H, Campbell C. Automated bone marrow cytology using deep learning to generate a histogram of cell types. Communications Medicine 2022;2(1) View
  7. Kubasch A, Grieb N, Oeser A, Haferlach C, Platzbecker U. RETRACTED ARTICLE: Einsatz von künstlicher Intelligenz im Management akuter Leukämien. Die Onkologie 2022;28(8):731 View
  8. Wang C, Wei X, Li C, Wang Y, Wu Y, Niu Y, Zhang C, Yu Y. Efficient and Highly Accurate Diagnosis of Malignant Hematological Diseases Based on Whole-Slide Images Using Deep Learning. Frontiers in Oncology 2022;12 View
  9. Tripathi S, Augustin A, Sukumaran R, Dheer S, Kim E. HematoNet: Expert level classification of bone marrow cytology morphology in hematological malignancy with deep learning. Artificial Intelligence in the Life Sciences 2022;2:100043 View
  10. Wang X, Wang Y, Qi C, Qiao S, Yang S, Wang R, Jin H, Zhang J. The Application of Morphogo in the Detection of Megakaryocytes from Bone Marrow Digital Images with Convolutional Neural Networks. Technology in Cancer Research & Treatment 2023;22 View
  11. Haferlach T, Walter W. Challenging gold standard hematology diagnostics through the introduction of whole genome sequencing and artificial intelligence. International Journal of Laboratory Hematology 2023;45(2):156 View
  12. Lee N, Jeong S, Park M, Song W. Deep learning application of the discrimination of bone marrow aspiration cells in patients with myelodysplastic syndromes. Scientific Reports 2022;12(1) View
  13. Yu C, Peng Y, Liu L, Wang X, Xiao Q, Ieracitano C. Leukemia can be Effectively Early Predicted in Routine Physical Examination with the Assistance of Machine Learning Models. Journal of Healthcare Engineering 2022;2022:1 View
  14. Li N, Fan L, Xu H, Zhang X, Bai Z, Li M, Xiong S, Jiang L, Yang J, Chen S, Qiao Y, Chen B. An AI-Aided Diagnostic Framework for Hematologic Neoplasms Based on Morphologic Features and Medical Expertise. Laboratory Investigation 2023;103(4):100055 View
  15. Nakamura I, Ida H, Yabuta M, Kashiwa W, Tsukamoto M, Sato S, Ota S, Kobayashi N, Masauzi H, Okada K, Kaga S, Miwa K, Kanai H, Masauzi N. Evaluation of two semi-supervised learning methods and their combination for automatic classification of bone marrow cells. Scientific Reports 2022;12(1) View
  16. Awada H, Gurnari C, Durmaz A, Awada H, Pagliuca S, Visconte V. Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic Syndromes. International Journal of Molecular Sciences 2022;23(5):2802 View
  17. Walter W, Pohlkamp C, Meggendorfer M, Nadarajah N, Kern W, Haferlach C, Haferlach T. Artificial intelligence in hematological diagnostics: Game changer or gadget?. Blood Reviews 2023;58:101019 View
  18. Guo L, Huang P, He H, Lu Q, Su Z, Zhang Q, Li J, Ma Q, Li J. A method to classify bone marrow cells with rejected option. Biomedical Engineering / Biomedizinische Technik 2022;67(3):227 View
  19. Kavitha R, Viswanathan N. Cat-Inspired Deep Convolutional Neural Network for Bone Marrow Cancer Cells Detection. Intelligent Automation & Soft Computing 2022;33(2):1305 View
  20. MORETTI M, CENCI A, PIVA E, CHIATAMONE RANIERI S, PAPA F. Morfologia digitale ed esame emocromocitometrico. La Rivista Italiana della Medicina di Laboratorio 2023;18(4) View
  21. Elsayed B, Elshoeibi A, Elhadary M, Ferih K, Elsabagh A, Rahhal A, Abu-Tineh M, Afana M, Abdulgayoom M, Yassin M. Applications of Artificial Intelligence in Philadelphia-Negative Myeloproliferative Neoplasms. Diagnostics 2023;13(6):1123 View
  22. Ramasamy M, Dhanaraj R, Pani S, Das R, Movassagh A, Gheisari M, Liu Y, Porkar P, Banu S. An improved deep convolutionary neural network for bone marrow cancer detection using image processing. Informatics in Medicine Unlocked 2023;38:101233 View
  23. Lv Z, Cao X, Jin X, Xu S, Deng H. High-accuracy morphological identification of bone marrow cells using deep learning-based Morphogo system. Scientific Reports 2023;13(1) View
  24. Cheng Z, Li Y. Improved YOLOv7 Algorithm for Detecting Bone Marrow Cells. Sensors 2023;23(17):7640 View
  25. Lewis J, Pozdnyakova O. Digital assessment of peripheral blood and bone marrow aspirate smears. International Journal of Laboratory Hematology 2023;45(S2):50 View
  26. Srisuwananukorn A, Salama M, Pearson A. Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary. Haematologica 2023;108(8):1993 View
  27. Lu Q, Wang B, He Q, Zhang Q, Guo L, Li J, Li J, Ma Q. A blood cell classification method based on MAE and active learning. Biomedical Signal Processing and Control 2024;90:105813 View
  28. Elshoeibi A, Badr A, Elsayed B, Metwally O, Elshoeibi R, Elhadary M, Elshoeibi A, Attya M, Khadadah F, Alshurafa A, Alhuraiji A, Yassin M. Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects. Cancers 2023;16(1):65 View
  29. Aydin Atasoy N, Faris Abdulla Al Rahhawi A. Examining the classification performance of pre‐trained capsule networks on imbalanced bone marrow cell dataset. International Journal of Imaging Systems and Technology 2024;34(3) View
  30. Lin Y, Chen Q, Chen T. Recent advancements in machine learning for bone marrow cell morphology analysis. Frontiers in Medicine 2024;11 View
  31. Hsu C, Yeh C, Huang I, Chen W, Peng Y, Tsai C, Ko M, Su C, Chen H, Wu W, Liu T, Lee K, Li C, Tu E, Huang W. Artificial intelligence interpretation of touch print smear cytology of testicular specimen from patients with azoospermia. Journal of Assisted Reproduction and Genetics 2024 View
  32. Kandasamy V, Simic V, Bacanin N, Pamucar D. Optimized deep learning networks for accurate identification of cancer cells in bone marrow. Neural Networks 2025;181:106822 View
  33. Wang S, Huang Z, Li J, Wu Y, Du J, Li T. Optimization of diagnosis and treatment of hematological diseases via artificial intelligence. Frontiers in Medicine 2024;11 View
  34. Yu T, Yang C, Hsu W, Hsu C, Wang H, Hsiao H, Chao H, Hsieh H, Wu J, Tsai Y, Chiang Y, Lee P, Lin C, Chen L, Sung Y, Yang Y, Yu C, Lin C, Kang C, Chang C, Chang H, Chu J, Cathy Kao K, Lin L, Wu M, Lin P, Yang P, Zhang Q, Chuang M, Chou S, Huang S, Cheng C, Yao C, Tien F, Yeh C, Chou W. A machine-learning-based algorithm for bone marrow cell differential counting. International Journal of Medical Informatics 2025;194:105692 View

Books/Policy Documents

  1. Davids J, Ashrafian H. Artificial Intelligence in Medicine. View
  2. Rivero-Palacio M, Alfonso-Morales W, Caicedo-Bravo E. Applications of Computational Intelligence. View
  3. Golts A, Livneh I, Zohar Y, Ciechanover A, Elad M. Computer Vision – ECCV 2022 Workshops. View