Investigation of the usefulness of a bile duct biopsy and bile cytology using a hyperspectral camera and machine learning.
Tomoko NoroseNobuyuki OhikeDaiki NakayaKentaro KamiyaYoshiya SugiuraMisato TakatsukiHirotaka KoizumiChie OkawaAya OhyaMiyu SasakiRuka AokiKazunari NakaharaShinjiro KobayashiKeisuke TateishiJunki KoikePublished in: Pathology international (2024)
To improve the efficiency of pathological diagnoses, the development of automatic pathological diagnostic systems using artificial intelligence (AI) is progressing; however, problems include the low interpretability of AI technology and the need for large amounts of data. We herein report the usefulness of a general-purpose method that combines a hyperspectral camera with machine learning. As a result of analyzing bile duct biopsy and bile cytology specimens, which are especially difficult to determine as benign or malignant, using multiple machine learning models, both were able to identify benign or malignant cells with an accuracy rate of more than 80% (93.3% for bile duct biopsy specimens and 83.2% for bile cytology specimens). This method has the potential to contribute to the diagnosis and treatment of bile duct cancer and is expected to be widely applied and utilized in general pathological diagnoses.
Keyphrases
- fine needle aspiration
- artificial intelligence
- machine learning
- big data
- ultrasound guided
- deep learning
- convolutional neural network
- induced apoptosis
- papillary thyroid
- mental health
- high speed
- cell cycle arrest
- electronic health record
- oxidative stress
- high resolution
- signaling pathway
- cell death
- endoplasmic reticulum stress
- young adults
- high grade
- risk assessment
- data analysis
- lymph node metastasis