418 lines
18 KiB
Python
418 lines
18 KiB
Python
"""影像报告与临床决策建议生成。"""
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from __future__ import annotations
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import logging
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import re
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from typing import Any
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from app.schemas.models import (
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DecisionRequest,
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DecisionResponse,
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Detection,
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ImagingReportRequest,
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ImagingReportResponse,
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RiskItem,
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SourceRef,
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)
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from app.services.llm_client import get_llm
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from app.services.rag_pipeline import get_rag
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logger = logging.getLogger(__name__)
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STUDY_LABEL = {
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"X_RAY": "X 光",
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"CT": "CT",
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"MRI": "MRI",
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"ULTRASOUND": "超声",
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}
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def build_imaging_texts(
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study_type: str,
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body_part: str,
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detections: list[Detection],
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mode: str,
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) -> tuple[str, str, str, float]:
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"""返回 findings, diagnosis, recommendations, confidence。"""
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st = STUDY_LABEL.get(study_type.upper(), study_type)
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part = body_part or "相关部位"
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if not detections:
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findings = f"{st}检查（{part}）：影像质量可评估，未见明确异常密度/信号灶。"
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diagnosis = f"{part}{st}未见明显异常"
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rec = "建议结合临床，必要时复查或进一步检查。"
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return findings, diagnosis, rec, 0.82
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lines = []
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for d in detections:
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name = d.label_zh or d.label
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lines.append(
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f"- 可见{name}样改变，框选区域约 ({int(d.bbox[0])},{int(d.bbox[1])})-"
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f"({int(d.bbox[2])},{int(d.bbox[3])})，模型置信度 {d.confidence:.0%}"
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)
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findings = f"{st}检查（{part}）AI 辅助读片所见：\n" + "\n".join(lines)
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top = max(detections, key=lambda x: x.confidence)
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diagnosis = f"{part}可疑{top.label_zh or top.label}，建议专科医师复核"
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rec = _rec_for_label(top.label)
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conf = sum(d.confidence for d in detections) / len(detections)
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if mode == "demo":
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findings += "\n（演示模式：检测框由 YOLO 演示引擎生成，非临床验证模型输出）"
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return findings, diagnosis, rec, round(min(0.98, conf), 4)
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def _rec_for_label(label: str) -> str:
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mapping = {
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"opacity": "建议结合血常规/炎症指标，必要时抗感染治疗并短期复查胸片。",
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"nodule": "建议按结节指南分层管理，3 个月后复查 CT，必要时多学科会诊。",
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"fracture": "建议骨科评估，必要时制动/固定，复查局部 X 光。",
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"effusion": "建议评估积液性质，必要时穿刺或超声随访。",
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"lesion": "建议结合临床与实验室检查，必要时增强扫描或专科转诊。",
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"mass": "建议进一步定性检查，排除占位性病变，及时专科就诊。",
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"calcification": "多为良性钙化可能，建议定期随访观察。",
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}
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return mapping.get(label, "建议专科医师综合临床资料判读，制定个体化方案。")
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def _normalize_multiline(text: str) -> str:
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"""把挤成一段的长文尽量拆成可读多行（句号/分号后换行，编号建议分行）。"""
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if not text:
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return ""
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s = str(text).strip()
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# 已有明显换行则只做空白整理
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if "\n" in s and s.count("\n") >= 2:
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return "\n".join(line.strip() for line in s.splitlines() if line.strip())
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# 编号建议：1. / 1、 / （1） 前换行
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s = re.sub(r"(?<![.\d])\s*([（(]?\d+[）).、])\s*", r"\n\1", s)
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# 中文段落分隔：句号/分号后跟新意时换行（保留较短从句）
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s = re.sub(r"([。；])\s*", r"\1\n", s)
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lines = [ln.strip() for ln in s.splitlines() if ln.strip()]
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# 合并过碎的短行（如单独标点）
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merged: list[str] = []
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for ln in lines:
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if merged and len(ln) <= 2 and not re.match(r"^[（(]?\d+", ln):
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merged[-1] = merged[-1] + ln
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else:
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merged.append(ln)
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return "\n".join(merged)
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def _normalize_recommendations(text: str) -> str:
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"""建议统一为多行编号列表。"""
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if not text:
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return ""
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s = str(text).strip()
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# 已是多行编号
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if re.search(r"(?m)^\s*[（(]?\d+[\.、）)]", s):
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return "\n".join(ln.strip() for ln in s.splitlines() if ln.strip())
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# 行内编号：1. / 1、 / （1）
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items = re.findall(
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r"[（(]?([1-9]\d?)[\.、）)]\s*([^（(]*?)(?=(?:[（(]?[1-9]\d?[\.、）)])|$)",
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s,
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)
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cleaned = [(idx, t.strip(" ；;。 \t")) for idx, t in items if t.strip(" ；;。 \t")]
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if len(cleaned) >= 2:
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return "\n".join(f"{i}. {t}" for i, (_, t) in enumerate(cleaned, 1))
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# 按分号切成条目
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chunks = [c.strip(" ；;。") for c in re.split(r"[；;]", s) if c.strip(" ；;。")]
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if len(chunks) >= 2:
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return "\n".join(f"{i}. {c}" for i, c in enumerate(chunks, 1))
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return s
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def make_full_report(
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study_type: str,
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body_part: str,
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findings: str,
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impression: str,
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recommendations: str,
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patient_summary: str = "",
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) -> str:
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"""结构化完整报告：固定四段，便于前端分段渲染。"""
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st = STUDY_LABEL.get(study_type.upper(), study_type)
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findings_n = _normalize_multiline(findings)
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impression_n = _normalize_multiline(impression) or impression
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rec_n = _normalize_recommendations(recommendations) or recommendations
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header = [
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"【影像诊断报告（AI 辅助）】",
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f"检查类型：{st}",
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f"检查部位：{body_part or '—'}",
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]
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if patient_summary:
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header.append(f"临床摘要：{patient_summary}")
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sections = [
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"\n".join(header),
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"一、影像所见\n" + (findings_n or "—"),
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"二、诊断印象\n" + (impression_n or "—"),
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"三、建议\n" + (rec_n or "—"),
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"四、声明\n本报告由 AI 辅助生成，仅供临床参考，需执业医师审核，不能替代正式报告。",
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]
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return "\n\n".join(sections)
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def generate_imaging_report(req: ImagingReportRequest) -> ImagingReportResponse:
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llm = get_llm()
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if llm.enabled:
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try:
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det_lines = []
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for d in req.detections:
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name = d.label_zh or d.label
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box = ",".join(str(int(x)) for x in d.bbox[:4]) if d.bbox else "-"
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det_lines.append(f"{name} conf={d.confidence:.0%} box=[{box}]")
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data = llm.chat_json(
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[
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{
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"role": "system",
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"content": (
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"你是三甲医院影像科辅助报告生成器。"
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"必须只输出一个 JSON 对象（不要 markdown 代码块），字段："
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"findings（影像所见：多段文字，用换行分隔；先写检查方法与部位，"
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"再写病灶描述，再写其余部位阴性所见，勿写成一整段）、"
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"impression（诊断印象：1～3 句，可换行）、"
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"recommendations（建议：必须用换行的编号列表，如 "
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"'1. ...\\n2. ...\\n3. ...'，含进一步检查/随访/会诊）、"
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"full_report 不要输出（由系统按分段模板拼接）。"
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"依据 YOLO 检测结果撰写，专业简洁；"
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"明确写明需执业医师审核，不能替代正式报告。"
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"禁止编造未提供的患者检验结果。"
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),
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},
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{
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"role": "user",
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"content": (
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f"检查类型={req.study_type}\n"
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f"检查部位={req.body_part or '未注明'}\n"
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f"患者摘要={req.patient_summary or '无'}\n"
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f"规则初诊={req.preliminary_diagnosis}\n"
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f"规则所见={req.findings}\n"
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f"检测列表:\n" + ("\n".join(det_lines) if det_lines else "（无检出）")
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),
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},
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]
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)
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findings = _normalize_multiline(str(data.get("findings") or req.findings).strip())
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impression = _normalize_multiline(
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str(
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data.get("impression")
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or data.get("preliminary_diagnosis")
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or req.preliminary_diagnosis
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).strip()
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)
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rec = _normalize_recommendations(
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str(data.get("recommendations") or "建议专科医师复核。").strip()
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)
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# 始终用分段模板拼完整报告，避免 LLM 输出一整段墙文本
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full = make_full_report(
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req.study_type, req.body_part, findings, impression, rec, req.patient_summary
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)
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model_name = llm.info().get("model") or "llm"
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logger.info("影像报告已由 LLM 生成 model=%s", model_name)
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return ImagingReportResponse(
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findings=findings,
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impression=impression,
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recommendations=rec,
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full_report=full,
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model_version=f"report-llm:{model_name}",
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)
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except Exception as e:
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logger.warning("影像报告 LLM 失败，回退模板: %s", e)
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logger.info("影像报告使用模板模式（LLM 未启用或调用失败） llm_enabled=%s", llm.enabled)
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# recommendations 留空，由 imaging API 回退到 build_imaging_texts 的按病灶建议
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full = make_full_report(
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req.study_type,
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req.body_part,
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req.findings,
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req.preliminary_diagnosis,
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"建议结合临床，由影像科/临床医师最终签发。",
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req.patient_summary,
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)
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return ImagingReportResponse(
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findings=req.findings,
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impression=req.preliminary_diagnosis,
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recommendations="",
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full_report=full,
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model_version="report-template",
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)
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def generate_decision(req: DecisionRequest) -> DecisionResponse:
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rag = get_rag()
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query = " ".join(
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x for x in [req.diagnosis, req.chief_complaint, req.history, req.imaging_summary] if x
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).strip() or "常见病辅助决策"
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sources = rag.retrieve(query, top_k=4)
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context = rag.build_context(sources)
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patient = req.patient
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patient_desc = ""
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if patient:
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patient_desc = f"年龄={patient.age} 性别={patient.gender} 姓名={patient.name or ''}"
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llm = get_llm()
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if llm.enabled:
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try:
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data = llm.chat_json(
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[
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{
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"role": "system",
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"content": (
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"你是临床辅助决策系统。输出严格 JSON，字段："
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"treatment_suggestions(数组，元素含 title,description,confidence),"
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"medication_suggestions(数组，元素含 name,dosage,category,confidence),"
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"nursing_advice(字符串数组),"
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"follow_up_plan(字符串数组),"
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"risks(数组，元素含 type,description,level,confidence),"
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"conflicts(字符串数组),"
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"full_text(字符串)。"
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"必须提醒需医师审核；勿编造不存在的检查结果。"
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),
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},
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{
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"role": "user",
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"content": (
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f"患者：{patient_desc}\n"
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f"主诉：{req.chief_complaint}\n"
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f"病史：{req.history}\n"
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f"查体：{req.exam_findings}\n"
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f"诊断：{req.diagnosis}\n"
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f"用药：{req.medications}\n"
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f"影像摘要：{req.imaging_summary}\n"
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f"知识库：\n{context or '无'}"
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),
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},
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]
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)
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return _map_decision(data, sources, engine="fastapi-rag+llm")
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except Exception as e:
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logger.warning("决策 LLM 失败: %s", e)
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return _template_decision(req, sources)
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def _map_decision(data: dict[str, Any], sources: list[SourceRef], engine: str) -> DecisionResponse:
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risks = []
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for r in data.get("risks") or []:
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if isinstance(r, dict):
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risks.append(
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RiskItem(
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type=str(r.get("type") or "风险"),
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description=str(r.get("description") or ""),
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level=str(r.get("level") or "中"),
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confidence=float(r.get("confidence") or 0.8),
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)
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)
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return DecisionResponse(
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treatment_suggestions=list(data.get("treatment_suggestions") or []),
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medication_suggestions=list(data.get("medication_suggestions") or []),
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nursing_advice=[str(x) for x in (data.get("nursing_advice") or [])],
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follow_up_plan=[str(x) for x in (data.get("follow_up_plan") or [])],
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risks=risks,
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conflicts=[str(x) for x in (data.get("conflicts") or [])],
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sources=sources,
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full_text=str(data.get("full_text") or ""),
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engine=engine,
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)
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def _template_decision(req: DecisionRequest, sources: list[SourceRef]) -> DecisionResponse:
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dx = req.diagnosis or ""
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treatments: list[dict[str, Any]] = []
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meds: list[dict[str, Any]] = []
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nursing: list[str] = []
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follow: list[str] = []
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risks: list[RiskItem] = []
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if "高血压" in dx:
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treatments = [
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{"title": "生活方式干预", "description": "低盐饮食，适量有氧运动，控制体重，戒烟限酒", "confidence": 0.95},
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{"title": "药物治疗", "description": "可考虑 ACEI/ARB 或 CCB 作为一线方案（需医师确认）", "confidence": 0.9},
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]
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meds = [
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{"name": "氨氯地平", "dosage": "5mg qd", "category": "钙通道阻滞剂", "confidence": 0.9},
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{"name": "缬沙坦", "dosage": "80mg qd", "category": "ARB", "confidence": 0.88},
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]
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nursing = ["监测血压并记录", "宣教服药依从性", "观察头晕、乏力等低血压症状"]
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follow = ["1–2 周门诊复查血压", "评估靶器官损害相关检查"]
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elif "糖尿病" in dx:
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treatments = [
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{"title": "饮食运动", "description": "控制总热量与碳水，规律运动", "confidence": 0.95},
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{"title": "降糖治疗", "description": "二甲双胍等一线方案需结合肾功能与禁忌", "confidence": 0.9},
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]
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meds = [{"name": "二甲双胍", "dosage": "0.5g tid", "category": "双胍类", "confidence": 0.92}]
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nursing = ["血糖监测指导", "足部护理宣教", "低血糖识别与处理"]
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follow = ["2–4 周复诊评估血糖", "定期查 HbA1c"]
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elif "肺炎" in dx or "阴影" in dx:
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treatments = [
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{"title": "抗感染", "description": "根据社区/医院获得性肺炎指南选择抗生素", "confidence": 0.88},
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{"title": "支持治疗", "description": "休息、补液、必要时氧疗", "confidence": 0.92},
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]
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meds = [{"name": "阿莫西林", "dosage": "0.5g tid", "category": "青霉素类", "confidence": 0.85}]
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nursing = ["监测体温与呼吸", "叩背排痰指导", "隔离防护宣教（如需要）"]
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follow = ["3–5 天评估疗效", "必要时复查胸片"]
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elif "结节" in dx:
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treatments = [
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{"title": "分层随访", "description": "按结节大小与特征选择随访或进一步检查", "confidence": 0.9},
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]
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nursing = ["戒烟宣教", "避免焦虑，说明随访意义"]
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follow = ["3 个月复查 CT", "出现咯血/胸痛及时就诊"]
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else:
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treatments = [
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{"title": "进一步评估", "description": "完善相关检查以明确诊断", "confidence": 0.85},
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{"title": "对症处理", "description": "根据症状给予相应支持治疗", "confidence": 0.88},
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]
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nursing = ["观察病情变化", "用药与生活方式宣教"]
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follow = ["按病情 1–2 周复诊", "出现加重症状及时急诊"]
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if req.patient and req.patient.age and req.patient.age >= 65:
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risks.append(
|
||
RiskItem(
|
||
type="高龄风险",
|
||
description="高龄患者需注意剂量调整、跌倒与多药联用风险",
|
||
level="高",
|
||
confidence=0.85,
|
||
)
|
||
)
|
||
if "高血压" in dx and req.patient and req.patient.age and req.patient.age > 60:
|
||
risks.append(
|
||
RiskItem(
|
||
type="心血管风险",
|
||
description="高血压合并高龄，心血管事件风险增加",
|
||
level="中",
|
||
confidence=0.8,
|
||
)
|
||
)
|
||
|
||
conflicts: list[str] = []
|
||
meds_text = req.medications or ""
|
||
if "华法林" in meds_text and "阿司匹林" in meds_text:
|
||
conflicts.append("警告：华法林与阿司匹林联合使用可能增加出血风险")
|
||
if "ACEI" in meds_text and "保钾" in meds_text:
|
||
conflicts.append("注意：ACEI 与保钾利尿剂联用可能致高钾血症")
|
||
|
||
src_hint = ""
|
||
if sources:
|
||
src_hint = "\n知识库参考：" + "；".join(s.title for s in sources[:3])
|
||
|
||
full = (
|
||
f"诊断相关辅助建议（规则+RAG）：{dx or '未明确'}\n"
|
||
f"治疗：{'; '.join(t['title'] for t in treatments)}\n"
|
||
f"护理：{'；'.join(nursing)}\n"
|
||
f"随访：{'；'.join(follow)}"
|
||
f"{src_hint}\n"
|
||
"（模板模式，可配置 LLM_API_KEY 启用大模型增强）"
|
||
)
|
||
return DecisionResponse(
|
||
treatment_suggestions=treatments,
|
||
medication_suggestions=meds,
|
||
nursing_advice=nursing,
|
||
follow_up_plan=follow,
|
||
risks=risks,
|
||
conflicts=conflicts,
|
||
sources=sources,
|
||
full_text=full,
|
||
engine="fastapi-rag-template",
|
||
)
|